p_table <- function(tab_data, ...) {
  tab_data_2 <- deparse(substitute(tab_data))
  
  table_p <- do.call(CreateTableOne, 
                     list(data = as.name(tab_data_2), includeNA = TRUE, ...))
  table_p_out <- print(table_p,
                       showAllLevels = TRUE,
                       printToggle = FALSE)
  kable(table_p_out,
        align = "c")
}
uni_var <- function(test_var, data_imp) {
                
        cat("_________________________________________________")
        cat("\n")
        cat("   \n##", test_var)
        cat("\n")
        cat("_________________________________________________")
        cat("\n")
        
        f <- as.formula(paste("Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 0)",
                              as.name(test_var),
                              sep = " ~ " ))
        
        data_imp_2 <- deparse(substitute(data_imp))
        km_fit <- do.call("survfit", list(formula = f, data = as.name(data_imp_2)))
        print(km_fit)
        cat("\n")
        print(summary(km_fit, times = c(12, 24, 36, 48, 60, 120)))
        cat("\n")
        cat("\n")
        cat("\n")
        cat("   \n## Univariable Cox Proportional Hazard Model for: ", test_var)
        cat("\n")
        cat("\n")
        n_levels <- nlevels(data_imp[[test_var]])
        if(n_levels == 1){
                print("Only one level, no Cox model performed")
                cat("\n")
        } else {
                cox_fit <- do.call("coxph", list(formula = f, data = as.name(data_imp_2)))
                print(summary(cox_fit))
                cat("\n")
                
                do.call("ggforest",
                         list(model = cox_fit, data = as.name(data_imp_2)))
        }
        cat("\n")
        cat("\n")
        cat("\n")
        cat("   \n## Unadjusted Kaplan Meier Overall Survival Curve for: ", test_var)
        p <- do.call("ggsurvplot",
                     list(fit = km_fit, data = as.name(data_imp_2),
                          palette = "jco", censor = FALSE, legend = "right",
                          linetype = "strata", xlab = "Time (Months)"))
        print(p)
}
f_plot <- function(test_var, data_imp){
                
        cat("_________________________________________________")
        cat("\n")
        cat("   \n##", test_var)
        cat("\n")
        cat("_________________________________________________")
        cat("\n")
        
        f <- as.formula(paste(as.name(test_var),
                              "AGE + SEX + T_SIZE + FACILITY_TYPE_F + FACILITY_LOCATION_F + YEAR_OF_DIAGNOSIS",
                              sep = " ~ " ))
        
        data_imp_2 <- deparse(substitute(data_imp))
        
        fit_fn <- do.call("glm", 
                       list(formula = f, 
                            data = as.name(data_imp_2), 
                            family = "binomial"))
        
        print(summary(fit_fn))
        
        or <- as.data.frame(exp(coefficients(fit_fn)))
        or$Variable <- rownames(or)
        rownames(or) <- c()
        names(or) <- c('OddsRatio', 'Variable')
        ci <- as.data.frame(exp(confint(fit_fn)))
        ci$Variable <- rownames(ci)
        rownames(ci) <- c()
        p_val_list <- tidy(fit_fn) %>%
        select(term, p.value) %>%
        rename(Variable = term) %>%
        mutate(p.value = round(p.value, 4))
        p_val_list$p.value <- as.character(p_val_list$p.value)
        p_val_list$p.value[p_val_list$p.value == "0"] <- "< 0.0001"
        all <- full_join(or, ci, by = 'Variable')
        all <- full_join(all, p_val_list, by = "Variable")
        names(all) <- c('OddsRatio', 'Variable', 'Lower', 'Upper', "p_value")
        all <- na.omit(all)
        all <- all %>%
        filter(Variable != '(Intercept)') 
        text <- cbind(c("Variable", as.character(all$Variable)), 
              c("Odds Ratio", as.character(round(all$OddsRatio, 2))),
              c("Lower CI", as.character(round(all$Lower, 2))),
              c("Upper CI", as.character(round(all$Upper, 2))),
              c("p Value", all$p_value))
        forestplot(text, 
           mean = c(NA, all$OddsRatio), 
           lower = c(NA, all$Lower), 
           upper = c(NA, all$Upper), 
           new_page =   TRUE, zero = 1, 
           clip = c(0.1, 100),
           hrzl_lines = list("2" = gpar(col="#444444")),
           vertices = TRUE,
           graph.pos = 2,
           xlab = "Odds Ratio (log)",
           align = "c",
           txt_gp = fpTxtGp(cex = 0.7),
           xticks = getTicks(low = all$Lower,
                             high = all$Upper,
                             clip=c(-Inf, Inf),
                             exp=TRUE),
           boxsize = 0.1)
    
}
col.width <- c(37, 10, 1, 1, 3, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 8, 2, 2, 2, 4, 4, 1, 4, 1, 1,
               1, 3, 2, 2, 8, 2, 5, 5, 5, 4, 5, 5, 5,4, 2, 1, 2, 1, 3, 1, 1, 1, 1, 1, 1, 3,
               3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 8,
               8, 8, 2, 1, 1, 1, 1, 8, 1, 1, 8, 1, 1, 2, 2, 5, 2, 5, 3, 1, 3, 1, 8, 8, 2, 8,
               2, 8, 2, 2, 1, 8, 1, 1, 1, 1, 1, 8, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 3, 1, 1,
               1, 1, 1, 1, 1, 1, 1)
col.names.abr <- c("PUF_CASE_ID", "PUF_FACILITY_ID", "FACILITY_TYPE_CD", "FACILITY_LOCATION_CD",
                   "AGE", "SEX", "RACE", "SPANISH_HISPANIC_ORIGIN", "INSURANCE_STATUS",
                   "MED_INC_QUAR_00", "NO_HSD_QUAR_00", "UR_CD_03", "MED_INC_QUAR_12", "NO_HSD_QUAR_12",
                   "UR_CD_13", "CROWFLY", "CDCC_TOTAL_BEST", "SEQUENCE_NUMBER", "CLASS_OF_CASE",
                   "YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "LATERALITY", "HISTOLOGY", "BEHAVIOR", "GRADE",
                   "DIAGNOSTIC_CONFIRMATION", "TUMOR_SIZE", "REGIONAL_NODES_POSITIVE",
                   "REGIONAL_NODES_EXAMINED", "DX_STAGING_PROC_DAYS", "RX_SUMM_DXSTG_PROC", "TNM_CLIN_T",
                   "TNM_CLIN_N", "TNM_CLIN_M", "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                   "TNM_PATH_STAGE_GROUP", "TNM_EDITION_NUMBER", "ANALYTIC_STAGE_GROUP", "CS_METS_AT_DX",
                   "CS_METS_EVAL", "CS_EXTENSION", "CS_TUMOR_SIZEEXT_EVAL", "CS_METS_DX_BONE", "CS_METS_DX_BRAIN",
                   "CS_METS_DX_LIVER", "CS_METS_DX_LUNG", "LYMPH_VASCULAR_INVASION", "CS_SITESPECIFIC_FACTOR_1",
                   "CS_SITESPECIFIC_FACTOR_2", "CS_SITESPECIFIC_FACTOR_3", "CS_SITESPECIFIC_FACTOR_4",
                   "CS_SITESPECIFIC_FACTOR_5", "CS_SITESPECIFIC_FACTOR_6", "CS_SITESPECIFIC_FACTOR_7",
                   "CS_SITESPECIFIC_FACTOR_8", "CS_SITESPECIFIC_FACTOR_9", "CS_SITESPECIFIC_FACTOR_10",
                   "CS_SITESPECIFIC_FACTOR_11", "CS_SITESPECIFIC_FACTOR_12", "CS_SITESPECIFIC_FACTOR_13",
                   "CS_SITESPECIFIC_FACTOR_14", "CS_SITESPECIFIC_FACTOR_15", "CS_SITESPECIFIC_FACTOR_16",
                   "CS_SITESPECIFIC_FACTOR_17", "CS_SITESPECIFIC_FACTOR_18", "CS_SITESPECIFIC_FACTOR_19",
                   "CS_SITESPECIFIC_FACTOR_20", "CS_SITESPECIFIC_FACTOR_21", "CS_SITESPECIFIC_FACTOR_22",
                   "CS_SITESPECIFIC_FACTOR_23", "CS_SITESPECIFIC_FACTOR_24", "CS_SITESPECIFIC_FACTOR_25",
                   "CS_VERSION_LATEST", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS", "DX_DEFSURG_STARTED_DAYS",
                   "RX_SUMM_SURG_PRIM_SITE", "RX_HOSP_SURG_APPR_2010", "RX_SUMM_SURGICAL_MARGINS",
                   "RX_SUMM_SCOPE_REG_LN_SUR", "RX_SUMM_SURG_OTH_REGDIS", "SURG_DISCHARGE_DAYS", "READM_HOSP_30_DAYS",
                   "REASON_FOR_NO_SURGERY", "DX_RAD_STARTED_DAYS", "RX_SUMM_RADIATION", "RAD_LOCATION_OF_RX",
                   "RAD_TREAT_VOL", "RAD_REGIONAL_RX_MODALITY", "RAD_REGIONAL_DOSE_CGY", "RAD_BOOST_RX_MODALITY",
                   "RAD_BOOST_DOSE_CGY", "RAD_NUM_TREAT_VOL", "RX_SUMM_SURGRAD_SEQ", "RAD_ELAPSED_RX_DAYS",
                   "REASON_FOR_NO_RADIATION", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS", "RX_SUMM_CHEMO",
                   "DX_HORMONE_STARTED_DAYS", "RX_SUMM_HORMONE", "DX_IMMUNO_STARTED_DAYS", "RX_SUMM_IMMUNOTHERAPY",
                   "RX_SUMM_TRNSPLNT_ENDO", "RX_SUMM_SYSTEMIC_SUR_SEQ", "DX_OTHER_STARTED_DAYS", "RX_SUMM_OTHER",
                   "PALLIATIVE_CARE", "RX_SUMM_TREATMENT_STATUS", "PUF_30_DAY_MORT_CD", "PUF_90_DAY_MORT_CD",
                   "DX_LASTCONTACT_DEATH_MONTHS", "PUF_VITAL_STATUS", "RX_HOSP_SURG_PRIM_SITE", "RX_HOSP_CHEMO",
                   "RX_HOSP_IMMUNOTHERAPY", "RX_HOSP_HORMONE", "RX_HOSP_OTHER", "PUF_MULT_SOURCE", "REFERENCE_DATE_FLAG",
                   "RX_SUMM_SCOPE_REG_LN_2012", "RX_HOSP_DXSTG_PROC", "PALLIATIVE_CARE_HOSP", "TUMOR_SIZE_SUMMARY",
                   "METS_AT_DX_OTHER", "METS_AT_DX_DISTANT_LN", "METS_AT_DX_BONE", "METS_AT_DX_BRAIN",
                   "METS_AT_DX_LIVER", "METS_AT_DX_LUNG", "NO_HSD_QUAR_16", "MED_INC_QUAR_16", "MEDICAID_EXPN_CODE")
#Read in data for each subsite
lip <- read_fwf('NCDBPUF_Lip.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
melanoma <- read_fwf('NCDBPUF_Melanoma.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
                       
skin <- read_fwf('NCDBPUF_OtSkin.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
hodgextr <- read_fwf('NCDBPUF_HodgExtr.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
hodgndal <- read_fwf('NCDBPUF_HodgNdal.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
NHLndal <- read_fwf('NCDBPUF_NHLNdal.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
NHLextr <- read_fwf('NCDBPUF_NHLExtr.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
breast <-  read_fwf('NCDBPUF_Breast.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
vulva <-  read_fwf('NCDBPUF_Vulva.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
vagina <-  read_fwf('NCDBPUF_Vagina.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
penis <-  read_fwf('NCDBPUF_Penis.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
otleuk <- read_fwf('NCDBPUF_OtLeuk.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
  
otheracuteleuk  <- read_fwf('NCDBPUF_OtAcLeuk.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
  
ALL  <- read_fwf('NCDBPUF_ALymLeuk.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
#Combine data for all subsites
dat <- bind_rows(lip, melanoma, skin, hodgextr, hodgndal, NHLndal, breast, 
                 vulva, vagina, penis, NHLextr, otleuk, otheracuteleuk, ALL)
rm(lip, melanoma, skin, hodgextr, hodgndal, NHLndal, breast, vulva, vagina, 
   penis, NHLextr, otleuk, otheracuteleuk, ALL)
prim_site_text <- data_frame(PRIMARY_SITE = c(
#NHL sites
"C000", 
"C001", 
"C002", 
"C003", 
"C004", 
"C005", 
"C006", 
"C008",
"C009", 
"C019", 
"C020", 
"C021",
"C022", 
"C023", 
"C024", 
"C028", 
"C029",
"C030",
"C031",
"C039", 
"C040", 
"C041", 
"C048",
"C049", 
"C050", 
"C051", 
"C052", 
"C058", 
"C059",
"C060", 
"C061", 
"C062", 
"C068", 
"C069", 
"C079",  
"C098",
"C099",
"C111",
"C142",
"C300",
"C379",
"C420",
"C422",
"C424",
#skin/melanoma
                                 "C440", "C441", "C442", "C443", "C444", "C445",
                                 "C446", "C447", "C448", "C449",
                                 
                                 #breast - nipple
                                 "C500",
                                 
                                 #vagina/vulva
                                 "C510", "C511", "C512", "C518", "C519", "C529",
                                 
                                 #penis
                                 "C600", "C601", "C602", "C608", "C609", "C639",
"C770",
"C771",
"C772",
"C773",
"C774",
"C775",
"C778",
"C779"),
SITE_TEXT = c(
"C00.0 External Lip: Upper NOS",
"C00.1 External Lip: Lower NOS",
"C00.2 External Lip: NOS",
"C00.3 Lip: Upper Mucosa",
"C00.4 Lip: Lower Mucosa",
"C00.5 Lip: Mucosa NOS",
"C00.6 Lip: Commissure",
"C00.8 Lip: Overlapping",
"C00.9 Lip NOS",
"C01.9 Tongue: Base NOS",
"C02.0 Tongue: Dorsal NOS",
"C02.1 Tongue: Border, Tip",
"C02.2 Tongue: Ventral NOS",
"C02.3 Tongue: Anterior NOS",
"C02.4 Lingual Tonsil",
"C02.8 Tongue: Overlapping",
"C02.9 Tongue: NOS",
"C03.0 Gum: Upper",
"C03.1 Gum: Lower",
"C03.9 Gum NOS",
"C04.0 Mouth: Anterior Floor",
"C04.1 Mouth: Lateral Floor",
"C04.8 Mouth: Overlapping Floor",
"C04.9 Floor of Mouth NOS",
"C05.0 Hard Palate",
"C05.1 Soft Palate NOS",
"C05.2 Uvula",
"C05.8 Palate: Overlapping",
"C05.9 Palate NOS",
"C06.0 Cheek Mucosa",
"C06.1 Mouth: Vestibule",
"C06.2 Retromolar Area",
"C06.8 Mouth: Other Overlapping",
"C06.9 Mouth NOS",
"C07.9 Parotid Gland",
  "C09.8 Tonsil: Overlapping",
  "C09.9 Tonsil NOS",
  "C11.1 Nasopharynx: Poster Wall", 
  "C14.2 Waldeyer Ring",
  "C30.0 Nasal Cavity",
  "C37.9 Thymus",
"C42.0 Blood",
  "C42.2 Spleen",
"C42.4 Hematopoietic NOS",
 #skin
"C44.0 Skin of lip, NOS",
"C44.1 Eyelid",
"C44.2 External ear",
"C44.3 Skin of ear and unspecified parts of face",
"C44.4 Skin of scalp and neck",
"C44.5 Skin of trunk",
"C44.6 Skin of upper limb and shoulder",
"C44.7 Skin of lower limb and hip",
"C44.8 Overlapping lesion of skin",
"C44.9 Skin, NOS", 
#breast
"C50.0 Nipple",
#vulva/vagina
"C51.0 Labium majus",
"C51.1 Labium minus",
"C51.2 Clitoris",
"C51.8 Overlapping lesion of vulva",
"C51.9 Vulva, NOS",
"C52.9 Vagina, NOS",
#penis
"C60.0 Prepuce",
"C60.1 Glans penis",
"C60.2 Body of penis",
"C60.8 Overlapping lesion of penis",
"C60.9 Penis",
"C63.2 Scrotum, NOS",
  "C77.0 Lymph Nodes: HeadFaceNeck",
  "C77.1 Intrathoracic Lymph Nodes",
  "C77.2 Intra-abdominal LymphNodes",
  "C77.3 Lymph Nodes of axilla or arm ",
  "C77.4 Lymph Nodes: Leg",
  "C77.5 Pelvic Lymph Nodes",
  "C77.8 Lymph Nodes: multiple region",
  "C77.9 Lymph Node NOS"))
dat <- merge(dat, prim_site_text, by = "PRIMARY_SITE", all.x = TRUE) 
rm(prim_site_text)
# convert numeric variables from character class to numeric class
num_vars <- c("AGE", "CROWFLY", "TUMOR_SIZE", "DX_STAGING_PROC_DAYS", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
              "DX_DEFSURG_STARTED_DAYS", "SURG_DISCHARGE_DAYS", "DX_RAD_STARTED_DAYS",  "RAD_REGIONAL_DOSE_CGY",
              "RAD_BOOST_DOSE_CGY", "RAD_ELAPSED_RX_DAYS", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS", 
              "DX_HORMONE_STARTED_DAYS", "DX_OTHER_STARTED_DAYS", "DX_LASTCONTACT_DEATH_MONTHS",
              "RAD_NUM_TREAT_VOL")
dat[num_vars] <- lapply(dat[num_vars], as.numeric)
# convert factor variables from character class to factor class
vars <- names(dat)
fact_vars <- vars[!(vars %in% num_vars)] # basically all of the non-numerics
dat[fact_vars] <- lapply(dat[fact_vars], as.character)
dat[fact_vars] <- lapply(dat[fact_vars], as.factor)
dat <- dat %>%
        mutate(FACILITY_TYPE_F = fct_recode(FACILITY_TYPE_CD,
                                            "Community Cancer Program" = "1",
                                            "Comprehensive Comm Ca Program" = "2",
                                            "Academic/Research Program" = "3",
                                            "Integrated Network Ca Program" = "4",
                                            "Other" = "9")) %>%
        mutate(FACILITY_LOCATION_F = fct_recode(FACILITY_LOCATION_CD,
                                            "New England" = "1",
                                            "Middle Atlantic" = "2",
                                            "South Atlantic" = "3",
                                            "East North Central" = "4",
                                            "East South Central" = "5",
                                            "West North Central" = "6",
                                            "West South Central" = "7",
                                            "Mountain" = "8",
                                            "Pacific" = "9",
                                            "out of US" = "0")) %>%
        mutate(FACILITY_GEOGRAPHY = fct_collapse(FACILITY_LOCATION_CD,
                                                 "Northeast" = c("1", "2"),
                                                 "South" = c("3", "7"),
                                                 "Midwest" = c("4", "5", "6"),
                                                 "West" = c("8", "9"))) %>%
        mutate(AGE_F = cut(AGE, c(0, 54, 64, 74, 100))) %>%
        mutate(AGE_40 = cut(AGE, c(0, 40, 100))) %>%
        mutate(SEX_F = fct_recode(SEX,
                                "Male" = "1",
                                "Female" = "2")) %>%
        mutate(RACE_F = fct_collapse(RACE,
                                "White" = c("01"),
                                "Black" = c("02"),
                                "Asian" = c("04", "05", "06", "07", "08", "10", "11", "12", "13", "14", "15",
                                            "16", "17", "20", "21", "22", "25", "26", "27", "28", "30", "31",
                                            "32", "96", "97"),
                                "Other/Unk" = c("03", "98", "99"))) %>%
        mutate(HISPANIC = fct_collapse(SPANISH_HISPANIC_ORIGIN,
                                       "Yes" = c("1", "2", "3", "4", "5", "6", "7", "8"),
                                       "No" = c("0"),
                                       "Unknown" = c("9"))) %>%
        mutate(INSURANCE_F = fct_recode(INSURANCE_STATUS,
                                         "None" = "0",
                                         "Private" = "1",
                                         "Medicaid" = "2",
                                         "Medicare" = "3",
                                         "Other Government" = "4",
                                         "Unknown" = "9")) %>%
        mutate(INSURANCE_F = fct_relevel(INSURANCE_F,
                                         "Private")) %>%
        mutate(INCOME_F = fct_recode(MED_INC_QUAR_12,
                                      "Less than $38,000" = "1",
                                      "$38,000 - $47,999" = "2",
                                      "$48,000 - $62,999" = "3",
                                      "$63,000 +" = "4")) %>%
        mutate(EDUCATION_F = fct_recode(NO_HSD_QUAR_12,
                                        "21% or more" = "1",
                                        "13 - 20.9%" = "2",
                                        "7 - 12.9%" = "3",
                                        "Less than 7%" = "4")) %>%
        mutate(U_R_F = fct_collapse(UR_CD_13,
                                    "Metro" = c("1", "2", "3"),
                                    "Urban" = c("4", "5", "6", "7"),
                                    "Rural" = c("8", "9"))) %>%
        mutate(CLASS_OF_CASE_F = fct_collapse(CLASS_OF_CASE,
                                              All_Part_Prim = c("10", "11", "12", "13",
                                                                "14", "20", "21", "22"),
                                              Other_Facility = c("00"))) %>%
        mutate(GRADE_F = fct_recode(GRADE,
                                  "Gr I: Well Diff" = "1",
                                  "Gr II: Mod Diff" = "2",
                                  "Gr III: Poor Diff" = "3",
                                  "Gr IV: Undiff/Anaplastic" = "4",
                                  "NA/Unkown" = "9")) %>%
        mutate(HISTOLOGY_F = fct_infreq(HISTOLOGY)) %>%
        mutate(HISTOLOGY_F = factor(HISTOLOGY_F)) %>%
        mutate(HISTOLOGY_F_LIM = fct_lump(HISTOLOGY_F, prop = 0.05)) %>%
        mutate(TNM_CLIN_T = fct_recode(TNM_CLIN_T,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_T = fct_relevel(TNM_CLIN_T,
                                        "1")) %>%
        mutate(TNM_CLIN_N = fct_recode(TNM_CLIN_N,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_M = fct_recode(TNM_CLIN_M,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_T = fct_recode(TNM_PATH_T,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_T = fct_relevel(TNM_PATH_T,
                                        "1")) %>%
        mutate(TNM_PATH_N = fct_recode(TNM_PATH_N,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_M = fct_recode(TNM_PATH_M,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_STAGE_GROUP = fct_recode(TNM_CLIN_STAGE_GROUP,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_STAGE_GROUP = fct_recode(TNM_PATH_STAGE_GROUP,
                                       "N_A" = "88")) %>%
        mutate(MARGINS = fct_recode(RX_SUMM_SURGICAL_MARGINS,
                                    "No Residual" = "0",
                                    "Residual, NOS" = "1",
                                    "Microscopic Resid" = "2",
                                    "Macroscopic Resid" = "3",
                                    "Not evaluable" = "7",
                                    "No surg" = "8",
                                    "Unknown" = "9")) %>%
        mutate(MARGINS_YN = fct_collapse(RX_SUMM_SURGICAL_MARGINS,
                                         "Yes" = c("1", "2", "3"),
                                         "No" = c("0"),
                                         "No surg/Unk/NA" = c("7", "8", "9"))) %>%
        mutate(READM_HOSP_30_DAYS_F = fct_recode(READM_HOSP_30_DAYS,
                                                 "No_Surg_or_No_Readmit" = "0",
                                                 "Unplan_Readmit_Same" = "1",
                                                 "Plan_Readmit_Same" = "2",
                                                 "PlanUnplan_Same" = "3",
                                                 "Unknown" = "4")) %>%
        mutate(RX_SUMM_RADIATION_F = fct_recode(RX_SUMM_RADIATION,
                                                "None" = "0",
                                                "Beam Radiation" = "1",
                                                "Radioactive Implants" = "2",
                                                "Radioisotopes" = "3",
                                                "Beam + Imp or Isotopes" = "4",
                                                "Radiation, NOS" = "5",
                                                "Unknown" = "9")) %>%
        mutate(PUF_30_DAY_MORT_CD_F = fct_recode(PUF_30_DAY_MORT_CD,
                                                 "Alive_30" = "0",
                                                 "Dead_30" = "1",
                                                 "Unknown" = "9")) %>%
        mutate(PUF_90_DAY_MORT_CD_F = fct_recode(PUF_90_DAY_MORT_CD,
                                                 "Alive_90" = "0",
                                                 "Dead_90" = "1",
                                                 "Unknown" = "9")) %>%
        mutate(LYMPH_VASCULAR_INVASION_F = fct_recode(LYMPH_VASCULAR_INVASION,
                                                      "Neg_LymphVasc_Inv" = "0",
                                                      "Pos_LumphVasc_Inv" = "1",
                                                      "N_A" = "8",
                                                      "Unknown" = "9")) %>%
        mutate(RX_HOSP_SURG_APPR_2010_F = fct_recode(RX_HOSP_SURG_APPR_2010,
                                                     "No_Surg" = "0",
                                                     "Robot_Assist" = "1",
                                                     "Robot_to_Open" = "2",
                                                     "Endo_Lap" = "3",
                                                     "Endo_Lap_to_Open" = "4",
                                                     "Open_Unknown" = "5",
                                                     "Unknown" = "9")) %>%
        mutate(All = "All") %>%
        mutate(All = factor(All)) %>%
        mutate(REASON_FOR_NO_SURGERY_F = fct_recode(REASON_FOR_NO_SURGERY,
                                                    "Surg performed" = "0",
                                                    "Surg not recommended" = "1",
                                                    "No surg due to pt factors" = "2",
                                                    "No surg, pt died" = "5",
                                                    "Surg rec, not done" = "6",
                                                    "Surg rec, pt refused" = "7",
                                                    "Surg rec, unk if done" = "8",
                                                    "Unknown" = "9")) %>%
        mutate(SURGERY_YN = ifelse(REASON_FOR_NO_SURGERY == "0",
                                   "Yes",
                                   ifelse(REASON_FOR_NO_SURGERY == "9",
                                          "Ukn",
                                          "No"))) %>%
        mutate(SURG_TF = case_when(SURGERY_YN == "Yes" ~ TRUE,
                             SURGERY_YN == "No" ~ FALSE,
                             TRUE ~ NA))  %>%
        mutate(REASON_FOR_NO_RADIATION_F = fct_recode(REASON_FOR_NO_RADIATION,
                                                    "Rad performed" = "0",
                                                    "Rad not recommended" = "1",
                                                    "No Rad due to pt factors" = "2",
                                                    "No Rad, pt died" = "5",
                                                    "Rad rec, not done" = "6",
                                                    "Rad rec, pt refused" = "7",
                                                    "Rad rec, unk if done" = "8",
                                                    "Unknown" = "9")) %>%
        mutate(RADIATION_YN = ifelse(REASON_FOR_NO_RADIATION == "0",
                                   "Yes",
                                   ifelse(REASON_FOR_NO_RADIATION == "9",
                                          NA,
                                          "No"))) %>%
        mutate(SURGRAD_SEQ_F = fct_recode(RX_SUMM_SURGRAD_SEQ,
                                                   "None or Surg or Rad" = "0",
                                                   "Rad before Surg" = "2",
                                                   "Surg before Rad" = "3",
                                                   "Rad before and after Surg" = "4",
                                                   "Intraop Rad" = "5",
                                                   "Intraop Rad plus other" = "6",
                                                   "Unknown" = "9")) %>%
        mutate(SURG_RAD_SEQ = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
                                     "Surg Alone",
                                     ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
                                            "Rad Alone",
                                            ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0",
                                                   "No Treatment",
                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "2",
                                                          "Rad then Surg",
                                                          ifelse(RX_SUMM_SURGRAD_SEQ == "3",
                                                                 "Surg then Rad",
                                                                 ifelse(RX_SUMM_SURGRAD_SEQ == "4",
                                                                        "Rad before and after Surg",
                                                                        "Other"))))))) %>%
        mutate(SURG_RAD_SEQ = fct_relevel(SURG_RAD_SEQ,
                                          "Surg Alone",
                                          "Surg then Rad",
                                          "Rad Alone")) %>%
        mutate(CHEMO_YN = fct_collapse(RX_SUMM_CHEMO,
                                       "No" = c("00", "82", "85", "86", "87"),
                                       "Yes" = c("01", "02", "03"),
                                       "Ukn" = c("88", "99"))) %>%
        mutate(IMMUNO_YN = fct_collapse(RX_SUMM_IMMUNOTHERAPY,
                                       "No" = c("00", "82", "85", "86", "87"),
                                       "Yes" = c("01"),
                                       "Ukn" = c("88", "99"))) %>%
        mutate(SURG_RAD_SEQ_C = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                     "Surg, No rad, No Chemo",
                                     ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                            "Rad, No Surg, No Chemo",
                                            ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                                   "No Surg, No Rad, No Chemo",
                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "No",
                                                          "Rad then Surg, No Chemo",
                                                          ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "No",
                                                                 "Surg then Rad, No Chemo",
                                                                 ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "No",
                                                                        "Rad before and after Surg, No Chemo",
                                ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                       "Surg, No rad, Yes Chemo",
                                       ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                              "Rad, No Surg, Yes Chemo",
                                              ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                                     "No Surg, No Rad, Yes Chemo",
                                                     ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "Yes",
                                                            "Rad then Surg, Yes Chemo",
                                                            ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "Yes",
                                                                   "Surg then Rad, Yes Chemo",
                                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "Yes",
                                                                          "Rad before and after Surg, Yes Chemo",
                                                                          "Other"))))))))))))) %>%
        mutate(SURG_RAD_SEQ_C = fct_infreq(SURG_RAD_SEQ_C)) %>%
        mutate(T_SIZE = as.numeric(TUMOR_SIZE)) %>%
        mutate(T_SIZE = ifelse(T_SIZE == 0,
                                "No Tumor",
                                ifelse(T_SIZE > 0 & T_SIZE < 10 | T_SIZE == 991,
                                       "< 1 cm",
                                       ifelse(T_SIZE >= 10 & T_SIZE < 20 | T_SIZE == 992,
                                              "1-2 cm",
                                              ifelse(T_SIZE >= 20 & T_SIZE < 30 | T_SIZE == 993,
                                                     "2-3 cm",
                                                     ifelse(T_SIZE >= 30 & T_SIZE < 40 | T_SIZE == 994,
                                                            "3-4 cm",
                                                            ifelse(T_SIZE >= 40 & T_SIZE < 50 | T_SIZE == 995,
                                                                   "4-5 cm",
                                                                   ifelse(T_SIZE >= 50 & T_SIZE < 60 | T_SIZE == 996,
                                                                          "5-6 cm",
                                                                          ifelse(T_SIZE >= 60 & T_SIZE <= 987 |
                                                                                         T_SIZE == 980 | T_SIZE == 989 |
                                                                                         T_SIZE == 997,
                                                                          ">6 cm",
                                                                          ifelse(T_SIZE == 988 | T_SIZE == 999,
                                                                                 "NA_unk",
                                                                                 "Microscopic focus")))))))))) %>%
        mutate(T_SIZE = factor(T_SIZE)) %>%
        mutate(T_SIZE = fct_relevel(T_SIZE,
                                     "No Tumor", "Microscopic focus", "< 1 cm", "1-2 cm", "2-3 cm", "3-4 cm",
                                       "4-5 cm", "5-6 cm", ">6 cm", "NA_unk")) %>%
        mutate(mets_at_dx = case_when(CS_METS_DX_LUNG == "1" ~ "Lung",
                                      CS_METS_DX_BONE == "1" ~ "Bone",
                                      CS_METS_DX_BRAIN == "1" ~ "Brain",
                                      CS_METS_DX_LIVER == "1" ~ "Liver",
                                      TRUE ~ "None/Other/Unk/NA")) %>%
        mutate(MEDICAID_EXPN_CODE = fct_recode(MEDICAID_EXPN_CODE,
                                               "Non-Expansion State" = "0",
                                               "Jan 2014 Expansion States" = "1",
                                               "Early Expansion States (2010-13)" = "2",
                                               "Late Expansion States (> Jan 2014)" = "3",
                                               "Suppressed for Ages 0 - 39" = "9"))  %>%
        mutate(EXPN_GROUP =  case_when(MEDICAID_EXPN_CODE  %in% c("Jan 2014 Expansion States") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Post-Expansion",
                                       
                                       MEDICAID_EXPN_CODE  %in% c("Jan 2014 Expansion States") & 
                                         YEAR_OF_DIAGNOSIS %in% 
                                          c("2004", "2005", "2006", "2007", "2008", 
                                            "2009", "2010", "2011", "2012", "2013") ~ "Pre-Expansion",
               
                                       MEDICAID_EXPN_CODE  %in% c("Early Expansion States (2010-13)") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2010", "2011", "2012", "2013", "2014", "2015") ~ "Post-Expansion",
                                       
                                        MEDICAID_EXPN_CODE  %in% c("Early Expansion States (2010-13)") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008", "2009") ~ "Pre-Expansion",
                                       MEDICAID_EXPN_CODE %in% c("Non-Expansion State") ~ "Pre-Expansion",
                                       MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") ~ "Pre-Expansion",
                    
                                       MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") & 
                                        YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Exclude",
                                       
                                       MEDICAID_EXPN_CODE == "Suppressed for Ages 0 - 39" ~ "Exclude")) %>%
  
  mutate(pre_2014 = YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008", 
                                            "2009", "2010", "2011", "2012", "2013")) %>%
  
  mutate(mets_at_dx_F = ifelse(mets_at_dx == "None/Other/Unk/NA", FALSE, TRUE)) %>% 
  
  mutate(Tx_YN = ifelse(SURG_RAD_SEQ == "No Treatment" & CHEMO_YN == "No" & 
                          IMMUNO_YN == "No", FALSE, 
                        ifelse(CHEMO_YN == "Ukn", NA, 
                               TRUE)))
fact_vars_2 <- c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "AGE_F", "SEX_F", "RACE_F",
                 "HISPANIC", "INSURANCE_F", "INCOME_F", "EDUCATION_F", "U_R_F",
                 "CDCC_TOTAL_BEST", "CLASS_OF_CASE_F", "YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "HISTOLOGY",
                 "BEHAVIOR", "GRADE_F", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M", "TNM_PATH_STAGE_GROUP",
                 "MARGINS", "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "mets_at_dx")
dat <- dat %>%
        mutate_at(fact_vars_2, funs(factor(.)))

Extract data of interest

# MPD
site_code <- c(
 #breast
  "C500", "C501", "C502","C503","C504","C505",
                                 "C506","C508","C509")
histo_code <- c("8540")
behavior_code <- c("3")
data <- dat %>%
        filter(BEHAVIOR %in% behavior_code) %>%
        filter(PRIMARY_SITE %in% site_code) %>%
        filter(HISTOLOGY %in% histo_code) %>%
#        filter(AGE >= 18) %>%
        filter(is.na(PUF_VITAL_STATUS) == FALSE) %>%
        filter(is.na(DX_LASTCONTACT_DEATH_MONTHS) == FALSE) %>%
        filter(SEQUENCE_NUMBER == "00") 
no_Excludes <- as.data.frame(data %>% 
                               filter(EXPN_GROUP != "Exclude") 
                             %>% droplevels())
#file_path <- c("/Users/beastatlife/Google Drive/Penn/Research/Barbieri/NCDB")
#save(data,
#      file = paste0(file_path, "/breast_data.Rda"))
#load("EMPD_data.Rda")

Data including skin tumors was obtained from the NCBD on October 7, 2019. Cases that were included in this analysis were those with:

  1. Site codes: C500, C501, C502, C503, C504, C505, C506, C508, C509
  2. Histology codes: 8540
  3. Behavior codes: 3

Patients were excluded if they didn’t have values for either follow up or vital status.

Patients were excluded if they had surgery to a distant site using RX_SUMM_SURG_OTH_REGDIS. This was done to avoid confounding of different surgical procedures. We are only interested in surgery at the primary site. These distant site surgeries were being counted in the surgery/radiation sequence and thus to simplify analysis they were removed.

data %>%
        CreateTableOne(data = .,
                     vars = c("RX_SUMM_SURG_OTH_REGDIS"),
                     includeNA = TRUE) %>%
        print(.,
              showAllLevels = TRUE)
                             
                              level Overall    
  n                                 700        
  RX_SUMM_SURG_OTH_REGDIS (%) 0     675 (96.4) 
                              1       6 ( 0.9) 
                              2       1 ( 0.1) 
                              3       0 ( 0.0) 
                              4       3 ( 0.4) 
                              5       0 ( 0.0) 
                              9      15 ( 2.1) 
data <- data %>%
        filter(RX_SUMM_SURG_OTH_REGDIS == "0") 

Race was grouped as white, black, asian, other/unknown Stage was grouped into 0, I, II, III, IV, NA_Unknown, stage 0 was removed Whether surgery was performed was based on the REASON_FOR_NO_SURGERY variable. The SURGERY_YN variable was classified as ‘Yes’, ‘No’, or ‘Unknown’.

Whether radiation was performed was based on the REASON_FOR_NO_RADIATION variable. The RADIATION_YN variable was classified as ‘Yes’, ‘No’, or ‘Unknown’.

##Table of variables for all cases:

p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT",  "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "SURGERY_YN", "RADIATION_YN", "CHEMO_YN", 
                 "IMMUNO_YN", "Tx_YN", "mets_at_dx",
                 "MEDICAID_EXPN_CODE", "EXPN_GROUP"))
level Overall
n 675
FACILITY_TYPE_F (%) Community Cancer Program 81 ( 12.0)
Comprehensive Comm Ca Program 296 ( 43.9)
Academic/Research Program 173 ( 25.6)
Integrated Network Ca Program 90 ( 13.3)
NA 35 ( 5.2)
FACILITY_LOCATION_F (%) New England 31 ( 4.6)
Middle Atlantic 93 ( 13.8)
South Atlantic 143 ( 21.2)
East North Central 134 ( 19.9)
East South Central 44 ( 6.5)
West North Central 59 ( 8.7)
West South Central 60 ( 8.9)
Mountain 35 ( 5.2)
Pacific 41 ( 6.1)
NA 35 ( 5.2)
FACILITY_GEOGRAPHY (%) Northeast 124 ( 18.4)
South 203 ( 30.1)
Midwest 237 ( 35.1)
West 76 ( 11.3)
NA 35 ( 5.2)
AGE (mean (sd)) 65.56 (15.04)
AGE_F (%) (0,54] 161 ( 23.9)
(54,64] 149 ( 22.1)
(64,74] 149 ( 22.1)
(74,100] 216 ( 32.0)
AGE_40 (%) (0,40] 38 ( 5.6)
(40,100] 637 ( 94.4)
SEX_F (%) Male 19 ( 2.8)
Female 656 ( 97.2)
RACE_F (%) White 570 ( 84.4)
Black 77 ( 11.4)
Other/Unk 16 ( 2.4)
Asian 12 ( 1.8)
HISPANIC (%) No 587 ( 87.0)
Yes 27 ( 4.0)
Unknown 61 ( 9.0)
INSURANCE_F (%) Private 273 ( 40.4)
None 24 ( 3.6)
Medicaid 35 ( 5.2)
Medicare 320 ( 47.4)
Other Government 8 ( 1.2)
Unknown 15 ( 2.2)
INCOME_F (%) Less than $38,000 130 ( 19.3)
$38,000 - $47,999 144 ( 21.3)
$48,000 - $62,999 178 ( 26.4)
$63,000 + 218 ( 32.3)
NA 5 ( 0.7)
EDUCATION_F (%) 21% or more 99 ( 14.7)
13 - 20.9% 164 ( 24.3)
7 - 12.9% 222 ( 32.9)
Less than 7% 186 ( 27.6)
NA 4 ( 0.6)
U_R_F (%) Metro 557 ( 82.5)
Urban 88 ( 13.0)
Rural 12 ( 1.8)
NA 18 ( 2.7)
CROWFLY (mean (sd)) 25.98 (113.56)
CDCC_TOTAL_BEST (%) 0 558 ( 82.7)
1 80 ( 11.9)
2 23 ( 3.4)
3 14 ( 2.1)
SITE_TEXT (%) C00.0 External Lip: Upper NOS 0 ( 0.0)
C00.1 External Lip: Lower NOS 0 ( 0.0)
C00.2 External Lip: NOS 0 ( 0.0)
C00.3 Lip: Upper Mucosa 0 ( 0.0)
C00.4 Lip: Lower Mucosa 0 ( 0.0)
C00.5 Lip: Mucosa NOS 0 ( 0.0)
C00.6 Lip: Commissure 0 ( 0.0)
C00.8 Lip: Overlapping 0 ( 0.0)
C00.9 Lip NOS 0 ( 0.0)
C01.9 Tongue: Base NOS 0 ( 0.0)
C02.0 Tongue: Dorsal NOS 0 ( 0.0)
C02.1 Tongue: Border, Tip 0 ( 0.0)
C02.2 Tongue: Ventral NOS 0 ( 0.0)
C02.3 Tongue: Anterior NOS 0 ( 0.0)
C02.4 Lingual Tonsil 0 ( 0.0)
C02.8 Tongue: Overlapping 0 ( 0.0)
C02.9 Tongue: NOS 0 ( 0.0)
C03.0 Gum: Upper 0 ( 0.0)
C03.1 Gum: Lower 0 ( 0.0)
C03.9 Gum NOS 0 ( 0.0)
C04.0 Mouth: Anterior Floor 0 ( 0.0)
C04.1 Mouth: Lateral Floor 0 ( 0.0)
C04.9 Floor of Mouth NOS 0 ( 0.0)
C05.0 Hard Palate 0 ( 0.0)
C05.1 Soft Palate NOS 0 ( 0.0)
C05.2 Uvula 0 ( 0.0)
C05.8 Palate: Overlapping 0 ( 0.0)
C05.9 Palate NOS 0 ( 0.0)
C06.0 Cheek Mucosa 0 ( 0.0)
C06.1 Mouth: Vestibule 0 ( 0.0)
C06.2 Retromolar Area 0 ( 0.0)
C06.8 Mouth: Other Overlapping 0 ( 0.0)
C06.9 Mouth NOS 0 ( 0.0)
C07.9 Parotid Gland 0 ( 0.0)
C09.8 Tonsil: Overlapping 0 ( 0.0)
C09.9 Tonsil NOS 0 ( 0.0)
C11.1 Nasopharynx: Poster Wall 0 ( 0.0)
C14.2 Waldeyer Ring 0 ( 0.0)
C30.0 Nasal Cavity 0 ( 0.0)
C37.9 Thymus 0 ( 0.0)
C42.0 Blood 0 ( 0.0)
C42.2 Spleen 0 ( 0.0)
C42.4 Hematopoietic NOS 0 ( 0.0)
C44.0 Skin of lip, NOS 0 ( 0.0)
C44.1 Eyelid 0 ( 0.0)
C44.2 External ear 0 ( 0.0)
C44.3 Skin of ear and unspecified parts of face 0 ( 0.0)
C44.4 Skin of scalp and neck 0 ( 0.0)
C44.5 Skin of trunk 0 ( 0.0)
C44.6 Skin of upper limb and shoulder 0 ( 0.0)
C44.7 Skin of lower limb and hip 0 ( 0.0)
C44.8 Overlapping lesion of skin 0 ( 0.0)
C44.9 Skin, NOS 0 ( 0.0)
C50.0 Nipple 448 ( 66.4)
C51.0 Labium majus 0 ( 0.0)
C51.1 Labium minus 0 ( 0.0)
C51.2 Clitoris 0 ( 0.0)
C51.8 Overlapping lesion of vulva 0 ( 0.0)
C51.9 Vulva, NOS 0 ( 0.0)
C52.9 Vagina, NOS 0 ( 0.0)
C60.0 Prepuce 0 ( 0.0)
C60.1 Glans penis 0 ( 0.0)
C60.2 Body of penis 0 ( 0.0)
C60.8 Overlapping lesion of penis 0 ( 0.0)
C60.9 Penis 0 ( 0.0)
C63.2 Scrotum, NOS 0 ( 0.0)
C77.0 Lymph Nodes: HeadFaceNeck 0 ( 0.0)
C77.1 Intrathoracic Lymph Nodes 0 ( 0.0)
C77.2 Intra-abdominal LymphNodes 0 ( 0.0)
C77.3 Lymph Nodes of axilla or arm 0 ( 0.0)
C77.4 Lymph Nodes: Leg 0 ( 0.0)
C77.5 Pelvic Lymph Nodes 0 ( 0.0)
C77.8 Lymph Nodes: multiple region 0 ( 0.0)
C77.9 Lymph Node NOS 0 ( 0.0)
NA 227 ( 33.6)
BEHAVIOR (%) 2 0 ( 0.0)
3 675 (100.0)
GRADE_F (%) Gr I: Well Diff 22 ( 3.3)
Gr II: Mod Diff 55 ( 8.1)
Gr III: Poor Diff 106 ( 15.7)
Gr IV: Undiff/Anaplastic 2 ( 0.3)
5 0 ( 0.0)
6 0 ( 0.0)
7 0 ( 0.0)
8 0 ( 0.0)
NA/Unkown 490 ( 72.6)
DX_STAGING_PROC_DAYS (mean (sd)) 1.68 (10.24)
TNM_CLIN_T (%) N_A 0 ( 0.0)
c0 5 ( 0.7)
c1 37 ( 5.5)
c1A 12 ( 1.8)
c1B 9 ( 1.3)
c1C 15 ( 2.2)
c1MI 4 ( 0.6)
c2 21 ( 3.1)
c2A 0 ( 0.0)
c2B 0 ( 0.0)
c2C 0 ( 0.0)
c2D 0 ( 0.0)
c3 18 ( 2.7)
c3A 0 ( 0.0)
c3B 0 ( 0.0)
c4 10 ( 1.5)
c4A 0 ( 0.0)
c4B 10 ( 1.5)
c4C 0 ( 0.0)
c4D 3 ( 0.4)
cX 188 ( 27.9)
pA 0 ( 0.0)
pIS 307 ( 45.5)
NA 36 ( 5.3)
TNM_CLIN_N (%) N_A 0 ( 0.0)
c0 449 ( 66.5)
c1 26 ( 3.9)
c1A 0 ( 0.0)
c1B 0 ( 0.0)
c2 6 ( 0.9)
c2A 2 ( 0.3)
c2B 1 ( 0.1)
c2C 0 ( 0.0)
c3 3 ( 0.4)
c3A 1 ( 0.1)
c3B 0 ( 0.0)
c3C 0 ( 0.0)
c4 0 ( 0.0)
cX 162 ( 24.0)
NA 25 ( 3.7)
TNM_CLIN_M (%) N_A 0 ( 0.0)
c0 614 ( 91.0)
c0I+ 0 ( 0.0)
c1 26 ( 3.9)
c1A 0 ( 0.0)
c1B 0 ( 0.0)
c1C 0 ( 0.0)
NA 35 ( 5.2)
TNM_CLIN_STAGE_GROUP (%) 0 333 ( 49.3)
1 39 ( 5.8)
1A 32 ( 4.7)
1B 1 ( 0.1)
1C 0 ( 0.0)
2 0 ( 0.0)
2A 23 ( 3.4)
2B 13 ( 1.9)
2C 0 ( 0.0)
3 2 ( 0.3)
3A 7 ( 1.0)
3B 12 ( 1.8)
3C 3 ( 0.4)
4 27 ( 4.0)
4A 0 ( 0.0)
4A1 0 ( 0.0)
4A2 0 ( 0.0)
4B 0 ( 0.0)
4C 0 ( 0.0)
N_A 0 ( 0.0)
99 183 ( 27.1)
TNM_PATH_T (%) N_A 0 ( 0.0)
p0 8 ( 1.2)
p1 13 ( 1.9)
p1A 17 ( 2.5)
p1B 11 ( 1.6)
p1C 24 ( 3.6)
p1MI 7 ( 1.0)
p2 12 ( 1.8)
p2A 0 ( 0.0)
p2B 0 ( 0.0)
p2C 0 ( 0.0)
p2D 0 ( 0.0)
p3 2 ( 0.3)
p3A 0 ( 0.0)
p3B 0 ( 0.0)
p4 1 ( 0.1)
p4A 0 ( 0.0)
p4B 5 ( 0.7)
p4C 0 ( 0.0)
p4D 3 ( 0.4)
pA 0 ( 0.0)
pIS 261 ( 38.7)
pX 253 ( 37.5)
NA 58 ( 8.6)
TNM_PATH_N (%) N_A 0 ( 0.0)
p0 233 ( 34.5)
p0I- 30 ( 4.4)
p0I+ 2 ( 0.3)
p0M- 0 ( 0.0)
p0M+ 0 ( 0.0)
p1 7 ( 1.0)
p1A 9 ( 1.3)
p1B 0 ( 0.0)
p1C 0 ( 0.0)
p1MI 2 ( 0.3)
p2 2 ( 0.3)
p2A 2 ( 0.3)
p2B 0 ( 0.0)
p2C 0 ( 0.0)
p3 2 ( 0.3)
p3A 3 ( 0.4)
p3B 0 ( 0.0)
p3C 0 ( 0.0)
p4 0 ( 0.0)
pX 304 ( 45.0)
NA 79 ( 11.7)
TNM_PATH_M (%) N_A 0 ( 0.0)
p0 0 ( 0.0)
p1 8 ( 1.2)
p1A 0 ( 0.0)
p1B 0 ( 0.0)
p1C 0 ( 0.0)
pX 344 ( 51.0)
NA 323 ( 47.9)
TNM_PATH_STAGE_GROUP (%) 0 298 ( 44.1)
1 55 ( 8.1)
1A 26 ( 3.9)
1B 2 ( 0.3)
1C 0 ( 0.0)
2 3 ( 0.4)
2A 21 ( 3.1)
2B 8 ( 1.2)
2C 0 ( 0.0)
3 0 ( 0.0)
3A 4 ( 0.6)
3B 6 ( 0.9)
3C 5 ( 0.7)
4 9 ( 1.3)
4A 0 ( 0.0)
4A1 0 ( 0.0)
4B 0 ( 0.0)
4C 0 ( 0.0)
N_A 0 ( 0.0)
99 198 ( 29.3)
NA 40 ( 5.9)
DX_RX_STARTED_DAYS (mean (sd)) 35.43 (34.86)
DX_SURG_STARTED_DAYS (mean (sd)) 38.84 (40.14)
DX_DEFSURG_STARTED_DAYS (mean (sd)) 46.51 (46.12)
MARGINS (%) No Residual 511 ( 75.7)
Residual, NOS 10 ( 1.5)
Microscopic Resid 11 ( 1.6)
Macroscopic Resid 3 ( 0.4)
Not evaluable 0 ( 0.0)
No surg 122 ( 18.1)
Unknown 18 ( 2.7)
MARGINS_YN (%) No 511 ( 75.7)
Yes 24 ( 3.6)
No surg/Unk/NA 140 ( 20.7)
SURG_DISCHARGE_DAYS (mean (sd)) 1.00 (4.05)
READM_HOSP_30_DAYS_F (%) No_Surg_or_No_Readmit 627 ( 92.9)
Unplan_Readmit_Same 15 ( 2.2)
Plan_Readmit_Same 18 ( 2.7)
PlanUnplan_Same 1 ( 0.1)
9 14 ( 2.1)
RX_SUMM_RADIATION_F (%) None 465 ( 68.9)
Beam Radiation 200 ( 29.6)
Radioactive Implants 2 ( 0.3)
Radioisotopes 0 ( 0.0)
Beam + Imp or Isotopes 0 ( 0.0)
Radiation, NOS 0 ( 0.0)
Unknown 8 ( 1.2)
PUF_30_DAY_MORT_CD_F (%) Alive_30 542 ( 80.3)
Dead_30 1 ( 0.1)
Unknown 6 ( 0.9)
NA 126 ( 18.7)
PUF_90_DAY_MORT_CD_F (%) Alive_90 536 ( 79.4)
Dead_90 3 ( 0.4)
Unknown 10 ( 1.5)
NA 126 ( 18.7)
DX_LASTCONTACT_DEATH_MONTHS (mean (sd)) 59.19 (42.79)
LYMPH_VASCULAR_INVASION_F (%) Neg_LymphVasc_Inv 162 ( 24.0)
Pos_LumphVasc_Inv 10 ( 1.5)
N_A 0 ( 0.0)
Unknown 154 ( 22.8)
NA 349 ( 51.7)
RX_HOSP_SURG_APPR_2010_F (%) No_Surg 96 ( 14.2)
Robot_Assist 0 ( 0.0)
Robot_to_Open 0 ( 0.0)
Endo_Lap 1 ( 0.1)
Endo_Lap_to_Open 0 ( 0.0)
Open_Unknown 229 ( 33.9)
Unknown 0 ( 0.0)
NA 349 ( 51.7)
SURG_RAD_SEQ (%) Surg Alone 363 ( 53.8)
Surg then Rad 181 ( 26.8)
Rad Alone 19 ( 2.8)
No Treatment 95 ( 14.1)
Other 15 ( 2.2)
Rad before and after Surg 0 ( 0.0)
Rad then Surg 2 ( 0.3)
SURG_RAD_SEQ_C (%) Surg, No rad, No Chemo 321 ( 47.6)
Surg then Rad, No Chemo 141 ( 20.9)
Surg then Rad, Yes Chemo 33 ( 4.9)
Surg, No rad, Yes Chemo 25 ( 3.7)
No Surg, No Rad, Yes Chemo 9 ( 1.3)
No Surg, No Rad, No Chemo 81 ( 12.0)
Other 46 ( 6.8)
Rad, No Surg, Yes Chemo 5 ( 0.7)
Rad, No Surg, No Chemo 12 ( 1.8)
Rad then Surg, Yes Chemo 2 ( 0.3)
Rad then Surg, No Chemo 0 ( 0.0)
Rad before and after Surg, Yes Chemo 0 ( 0.0)
Rad before and after Surg, No Chemo 0 ( 0.0)
SURGERY_YN (%) No 115 ( 17.0)
Ukn 9 ( 1.3)
Yes 551 ( 81.6)
RADIATION_YN (%) No 466 ( 69.0)
Yes 202 ( 29.9)
NA 7 ( 1.0)
CHEMO_YN (%) No 563 ( 83.4)
Yes 74 ( 11.0)
Ukn 38 ( 5.6)
IMMUNO_YN (%) No 657 ( 97.3)
Yes 5 ( 0.7)
Ukn 13 ( 1.9)
Tx_YN (%) FALSE 80 ( 11.9)
TRUE 557 ( 82.5)
NA 38 ( 5.6)
mets_at_dx (%) Bone 10 ( 1.5)
Brain 0 ( 0.0)
Liver 1 ( 0.1)
Lung 4 ( 0.6)
None/Other/Unk/NA 660 ( 97.8)
MEDICAID_EXPN_CODE (%) Non-Expansion State 261 ( 38.7)
Jan 2014 Expansion States 207 ( 30.7)
Early Expansion States (2010-13) 77 ( 11.4)
Late Expansion States (> Jan 2014) 95 ( 14.1)
Suppressed for Ages 0 - 39 35 ( 5.2)
EXPN_GROUP (%) Exclude 35 ( 5.2)
Post-Expansion 62 ( 9.2)
Pre-Expansion 578 ( 85.6)
p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN", "CHEMO_YN",
                 "IMMUNO_YN", "Tx_YN", "mets_at_dx",
                 "MEDICAID_EXPN_CODE"), 
        strata = "SURGERY_YN")
no non-missing arguments to min; returning Infno non-missing arguments to min; returning Infno non-missing arguments to min; returning Infno non-missing arguments to max; returning -Infno non-missing arguments to max; returning -Infno non-missing arguments to max; returning -Infno non-missing arguments to min; returning Infno non-missing arguments to min; returning Infno non-missing arguments to min; returning Infno non-missing arguments to max; returning -Infno non-missing arguments to max; returning -Infno non-missing arguments to max; returning -InfVariable has only NA's in at least one stratum. na.rm turned off.Variable has only NA's in at least one stratum. na.rm turned off.Variable has only NA's in at least one stratum. na.rm turned off.

level No Ukn Yes p test
n 115 9 551
FACILITY_TYPE_F (%) Community Cancer Program 18 ( 15.7) 3 ( 33.3) 60 ( 10.9) 0.279
Comprehensive Comm Ca Program 49 ( 42.6) 3 ( 33.3) 244 ( 44.3)
Academic/Research Program 32 ( 27.8) 2 ( 22.2) 139 ( 25.2)
Integrated Network Ca Program 14 ( 12.2) 1 ( 11.1) 75 ( 13.6)
NA 2 ( 1.7) 0 ( 0.0) 33 ( 6.0)
FACILITY_LOCATION_F (%) New England 10 ( 8.7) 2 ( 22.2) 19 ( 3.4) 0.008
Middle Atlantic 23 ( 20.0) 3 ( 33.3) 67 ( 12.2)
South Atlantic 23 ( 20.0) 0 ( 0.0) 120 ( 21.8)
East North Central 21 ( 18.3) 1 ( 11.1) 112 ( 20.3)
East South Central 7 ( 6.1) 0 ( 0.0) 37 ( 6.7)
West North Central 9 ( 7.8) 1 ( 11.1) 49 ( 8.9)
West South Central 15 ( 13.0) 1 ( 11.1) 44 ( 8.0)
Mountain 2 ( 1.7) 0 ( 0.0) 33 ( 6.0)
Pacific 3 ( 2.6) 1 ( 11.1) 37 ( 6.7)
NA 2 ( 1.7) 0 ( 0.0) 33 ( 6.0)
FACILITY_GEOGRAPHY (%) Northeast 33 ( 28.7) 5 ( 55.6) 86 ( 15.6) 0.001
South 38 ( 33.0) 1 ( 11.1) 164 ( 29.8)
Midwest 37 ( 32.2) 2 ( 22.2) 198 ( 35.9)
West 5 ( 4.3) 1 ( 11.1) 70 ( 12.7)
NA 2 ( 1.7) 0 ( 0.0) 33 ( 6.0)
AGE (mean (sd)) 70.50 (15.48) 73.44 (12.27) 64.40 (14.76) <0.001
AGE_F (%) (0,54] 20 ( 17.4) 0 ( 0.0) 141 ( 25.6) 0.003
(54,64] 27 ( 23.5) 3 ( 33.3) 119 ( 21.6)
(64,74] 15 ( 13.0) 2 ( 22.2) 132 ( 24.0)
(74,100] 53 ( 46.1) 4 ( 44.4) 159 ( 28.9)
AGE_40 (%) (0,40] 3 ( 2.6) 0 ( 0.0) 35 ( 6.4) 0.217
(40,100] 112 ( 97.4) 9 (100.0) 516 ( 93.6)
SEX_F (%) Male 2 ( 1.7) 0 ( 0.0) 17 ( 3.1) 0.639
Female 113 ( 98.3) 9 (100.0) 534 ( 96.9)
RACE_F (%) White 89 ( 77.4) 7 ( 77.8) 474 ( 86.0) 0.280
Black 20 ( 17.4) 2 ( 22.2) 55 ( 10.0)
Other/Unk 4 ( 3.5) 0 ( 0.0) 12 ( 2.2)
Asian 2 ( 1.7) 0 ( 0.0) 10 ( 1.8)
HISPANIC (%) No 102 ( 88.7) 9 (100.0) 476 ( 86.4) 0.262
Yes 7 ( 6.1) 0 ( 0.0) 20 ( 3.6)
Unknown 6 ( 5.2) 0 ( 0.0) 55 ( 10.0)
INSURANCE_F (%) Private 32 ( 27.8) 3 ( 33.3) 238 ( 43.2) 0.014
None 8 ( 7.0) 0 ( 0.0) 16 ( 2.9)
Medicaid 9 ( 7.8) 1 ( 11.1) 25 ( 4.5)
Medicare 61 ( 53.0) 4 ( 44.4) 255 ( 46.3)
Other Government 0 ( 0.0) 0 ( 0.0) 8 ( 1.5)
Unknown 5 ( 4.3) 1 ( 11.1) 9 ( 1.6)
INCOME_F (%) Less than $38,000 29 ( 25.2) 2 ( 22.2) 99 ( 18.0) 0.277
$38,000 - $47,999 22 ( 19.1) 0 ( 0.0) 122 ( 22.1)
$48,000 - $62,999 32 ( 27.8) 4 ( 44.4) 142 ( 25.8)
$63,000 + 30 ( 26.1) 3 ( 33.3) 185 ( 33.6)
NA 2 ( 1.7) 0 ( 0.0) 3 ( 0.5)
EDUCATION_F (%) 21% or more 28 ( 24.3) 2 ( 22.2) 69 ( 12.5) 0.048
13 - 20.9% 26 ( 22.6) 2 ( 22.2) 136 ( 24.7)
7 - 12.9% 35 ( 30.4) 2 ( 22.2) 185 ( 33.6)
Less than 7% 24 ( 20.9) 3 ( 33.3) 159 ( 28.9)
NA 2 ( 1.7) 0 ( 0.0) 2 ( 0.4)
U_R_F (%) Metro 97 ( 84.3) 9 (100.0) 451 ( 81.9) 0.833
Urban 14 ( 12.2) 0 ( 0.0) 74 ( 13.4)
Rural 1 ( 0.9) 0 ( 0.0) 11 ( 2.0)
NA 3 ( 2.6) 0 ( 0.0) 15 ( 2.7)
CROWFLY (mean (sd)) 14.49 (19.23) 28.98 (60.03) 28.29 (124.92) 0.499
CDCC_TOTAL_BEST (%) 0 90 ( 78.3) 6 ( 66.7) 462 ( 83.8) 0.060
1 13 ( 11.3) 2 ( 22.2) 65 ( 11.8)
2 7 ( 6.1) 0 ( 0.0) 16 ( 2.9)
3 5 ( 4.3) 1 ( 11.1) 8 ( 1.5)
SITE_TEXT (%) C00.0 External Lip: Upper NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) NaN
C00.1 External Lip: Lower NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.2 External Lip: NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.3 Lip: Upper Mucosa 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.4 Lip: Lower Mucosa 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.5 Lip: Mucosa NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.6 Lip: Commissure 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.8 Lip: Overlapping 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.9 Lip NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C01.9 Tongue: Base NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.0 Tongue: Dorsal NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.1 Tongue: Border, Tip 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.2 Tongue: Ventral NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.3 Tongue: Anterior NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.4 Lingual Tonsil 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.8 Tongue: Overlapping 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.9 Tongue: NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C03.0 Gum: Upper 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C03.1 Gum: Lower 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C03.9 Gum NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C04.0 Mouth: Anterior Floor 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C04.1 Mouth: Lateral Floor 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C04.9 Floor of Mouth NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C05.0 Hard Palate 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C05.1 Soft Palate NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C05.2 Uvula 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C05.8 Palate: Overlapping 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C05.9 Palate NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C06.0 Cheek Mucosa 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C06.1 Mouth: Vestibule 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C06.2 Retromolar Area 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C06.8 Mouth: Other Overlapping 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C06.9 Mouth NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C07.9 Parotid Gland 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C09.8 Tonsil: Overlapping 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C09.9 Tonsil NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C11.1 Nasopharynx: Poster Wall 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C14.2 Waldeyer Ring 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C30.0 Nasal Cavity 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C37.9 Thymus 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C42.0 Blood 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C42.2 Spleen 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C42.4 Hematopoietic NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.0 Skin of lip, NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.1 Eyelid 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.2 External ear 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.3 Skin of ear and unspecified parts of face 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.4 Skin of scalp and neck 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.5 Skin of trunk 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.6 Skin of upper limb and shoulder 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.7 Skin of lower limb and hip 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.8 Overlapping lesion of skin 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.9 Skin, NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C50.0 Nipple 49 ( 42.6) 6 ( 66.7) 393 ( 71.3)
C51.0 Labium majus 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C51.1 Labium minus 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C51.2 Clitoris 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C51.8 Overlapping lesion of vulva 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C51.9 Vulva, NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C52.9 Vagina, NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C60.0 Prepuce 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C60.1 Glans penis 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C60.2 Body of penis 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C60.8 Overlapping lesion of penis 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C60.9 Penis 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C63.2 Scrotum, NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.0 Lymph Nodes: HeadFaceNeck 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.1 Intrathoracic Lymph Nodes 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.2 Intra-abdominal LymphNodes 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.3 Lymph Nodes of axilla or arm 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.4 Lymph Nodes: Leg 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.5 Pelvic Lymph Nodes 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.8 Lymph Nodes: multiple region 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.9 Lymph Node NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
NA 66 ( 57.4) 3 ( 33.3) 158 ( 28.7)
BEHAVIOR (%) 2 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) NaN
3 115 (100.0) 9 (100.0) 551 (100.0)
GRADE_F (%) Gr I: Well Diff 8 ( 7.0) 0 ( 0.0) 14 ( 2.5) NaN
Gr II: Mod Diff 7 ( 6.1) 0 ( 0.0) 48 ( 8.7)
Gr III: Poor Diff 17 ( 14.8) 1 ( 11.1) 88 ( 16.0)
Gr IV: Undiff/Anaplastic 0 ( 0.0) 0 ( 0.0) 2 ( 0.4)
5 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
6 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
7 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
8 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
NA/Unkown 83 ( 72.2) 8 ( 88.9) 399 ( 72.4)
DX_STAGING_PROC_DAYS (mean (sd)) 3.10 (13.51) 0.43 (1.13) 1.36 (9.37) 0.322
TNM_CLIN_T (%) N_A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) NaN
c0 2 ( 1.7) 0 ( 0.0) 3 ( 0.5)
c1 7 ( 6.1) 0 ( 0.0) 30 ( 5.4)
c1A 1 ( 0.9) 0 ( 0.0) 11 ( 2.0)
c1B 4 ( 3.5) 1 ( 11.1) 4 ( 0.7)
c1C 5 ( 4.3) 0 ( 0.0) 10 ( 1.8)
c1MI 0 ( 0.0) 0 ( 0.0) 4 ( 0.7)
c2 7 ( 6.1) 0 ( 0.0) 14 ( 2.5)
c2A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c2B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c2C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c2D 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c3 5 ( 4.3) 0 ( 0.0) 13 ( 2.4)
c3A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c3B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c4 9 ( 7.8) 0 ( 0.0) 1 ( 0.2)
c4A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c4B 4 ( 3.5) 1 ( 11.1) 5 ( 0.9)
c4C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c4D 2 ( 1.7) 0 ( 0.0) 1 ( 0.2)
cX 33 ( 28.7) 1 ( 11.1) 154 ( 27.9)
pA 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
pIS 26 ( 22.6) 5 ( 55.6) 276 ( 50.1)
NA 10 ( 8.7) 1 ( 11.1) 25 ( 4.5)
TNM_CLIN_N (%) N_A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) NaN
c0 55 ( 47.8) 6 ( 66.7) 388 ( 70.4)
c1 12 ( 10.4) 0 ( 0.0) 14 ( 2.5)
c1A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c1B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c2 3 ( 2.6) 1 ( 11.1) 2 ( 0.4)
c2A 1 ( 0.9) 0 ( 0.0) 1 ( 0.2)
c2B 1 ( 0.9) 0 ( 0.0) 0 ( 0.0)
c2C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c3 3 ( 2.6) 0 ( 0.0) 0 ( 0.0)
c3A 0 ( 0.0) 0 ( 0.0) 1 ( 0.2)
c3B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c3C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c4 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
cX 31 ( 27.0) 1 ( 11.1) 130 ( 23.6)
NA 9 ( 7.8) 1 ( 11.1) 15 ( 2.7)
TNM_CLIN_M (%) N_A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) NaN
c0 78 ( 67.8) 8 ( 88.9) 528 ( 95.8)
c0I+ 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c1 23 ( 20.0) 0 ( 0.0) 3 ( 0.5)
c1A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c1B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
NA 14 ( 12.2) 1 ( 11.1) 20 ( 3.6)
TNM_CLIN_STAGE_GROUP (%) 0 31 ( 27.0) 4 ( 44.4) 298 ( 54.1) NaN
1 8 ( 7.0) 1 ( 11.1) 30 ( 5.4)
1A 4 ( 3.5) 0 ( 0.0) 28 ( 5.1)
1B 0 ( 0.0) 0 ( 0.0) 1 ( 0.2)
1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
2 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
2A 4 ( 3.5) 0 ( 0.0) 19 ( 3.4)
2B 5 ( 4.3) 0 ( 0.0) 8 ( 1.5)
2C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
3 1 ( 0.9) 0 ( 0.0) 1 ( 0.2)
3A 2 ( 1.7) 1 ( 11.1) 4 ( 0.7)
3B 6 ( 5.2) 0 ( 0.0) 6 ( 1.1)
3C 1 ( 0.9) 0 ( 0.0) 2 ( 0.4)
4 24 ( 20.9) 0 ( 0.0) 3 ( 0.5)
4A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
4A1 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
4A2 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
4B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
4C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
N_A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
99 29 ( 25.2) 3 ( 33.3) 151 ( 27.4)
TNM_PATH_T (%) N_A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) NaN
p0 1 ( 0.9) 0 ( 0.0) 7 ( 1.3)
p1 0 ( 0.0) 0 ( 0.0) 13 ( 2.4)
p1A 0 ( 0.0) 0 ( 0.0) 17 ( 3.1)
p1B 0 ( 0.0) 0 ( 0.0) 11 ( 2.0)
p1C 1 ( 0.9) 0 ( 0.0) 23 ( 4.2)
p1MI 0 ( 0.0) 0 ( 0.0) 7 ( 1.3)
p2 0 ( 0.0) 0 ( 0.0) 12 ( 2.2)
p2A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p2B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p2C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p2D 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p3 0 ( 0.0) 0 ( 0.0) 2 ( 0.4)
p3A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p3B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p4 1 ( 0.9) 0 ( 0.0) 0 ( 0.0)
p4A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p4B 1 ( 0.9) 0 ( 0.0) 4 ( 0.7)
p4C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p4D 1 ( 0.9) 0 ( 0.0) 2 ( 0.4)
pA 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
pIS 5 ( 4.3) 0 ( 0.0) 256 ( 46.5)
pX 69 ( 60.0) 4 ( 44.4) 180 ( 32.7)
NA 36 ( 31.3) 5 ( 55.6) 17 ( 3.1)
TNM_PATH_N (%) N_A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) NaN
p0 2 ( 1.7) 0 ( 0.0) 231 ( 41.9)
p0I- 0 ( 0.0) 0 ( 0.0) 30 ( 5.4)
p0I+ 1 ( 0.9) 0 ( 0.0) 1 ( 0.2)
p0M- 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p0M+ 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1 2 ( 1.7) 0 ( 0.0) 5 ( 0.9)
p1A 1 ( 0.9) 0 ( 0.0) 8 ( 1.5)
p1B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1MI 0 ( 0.0) 0 ( 0.0) 2 ( 0.4)
p2 0 ( 0.0) 0 ( 0.0) 2 ( 0.4)
p2A 1 ( 0.9) 0 ( 0.0) 1 ( 0.2)
p2B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p2C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p3 0 ( 0.0) 0 ( 0.0) 2 ( 0.4)
p3A 0 ( 0.0) 0 ( 0.0) 3 ( 0.5)
p3B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p3C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p4 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
pX 71 ( 61.7) 4 ( 44.4) 229 ( 41.6)
NA 37 ( 32.2) 5 ( 55.6) 37 ( 6.7)
TNM_PATH_M (%) N_A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) NaN
p0 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1 5 ( 4.3) 0 ( 0.0) 3 ( 0.5)
p1A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
pX 49 ( 42.6) 3 ( 33.3) 292 ( 53.0)
NA 61 ( 53.0) 6 ( 66.7) 256 ( 46.5)
TNM_PATH_STAGE_GROUP (%) 0 2 ( 1.7) 0 ( 0.0) 296 ( 53.7) NaN
1 0 ( 0.0) 0 ( 0.0) 55 ( 10.0)
1A 0 ( 0.0) 0 ( 0.0) 26 ( 4.7)
1B 0 ( 0.0) 0 ( 0.0) 2 ( 0.4)
1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
2 0 ( 0.0) 0 ( 0.0) 3 ( 0.5)
2A 1 ( 0.9) 0 ( 0.0) 20 ( 3.6)
2B 0 ( 0.0) 0 ( 0.0) 8 ( 1.5)
2C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
3 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
3A 1 ( 0.9) 0 ( 0.0) 3 ( 0.5)
3B 0 ( 0.0) 0 ( 0.0) 6 ( 1.1)
3C 1 ( 0.9) 0 ( 0.0) 4 ( 0.7)
4 5 ( 4.3) 0 ( 0.0) 4 ( 0.7)
4A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
4A1 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
4B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
4C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
N_A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
99 81 ( 70.4) 7 ( 77.8) 110 ( 20.0)
NA 24 ( 20.9) 2 ( 22.2) 14 ( 2.5)
DX_RX_STARTED_DAYS (mean (sd)) 56.72 (74.66) 60.00 (NA) 33.70 (29.01) NA
DX_SURG_STARTED_DAYS (mean (sd)) NaN (NA) NaN (NA) 38.84 (40.14) NA
DX_DEFSURG_STARTED_DAYS (mean (sd)) NaN (NA) NaN (NA) 46.51 (46.12) NA
MARGINS (%) No Residual 0 ( 0.0) 0 ( 0.0) 511 ( 92.7) NaN
Residual, NOS 0 ( 0.0) 0 ( 0.0) 10 ( 1.8)
Microscopic Resid 0 ( 0.0) 0 ( 0.0) 11 ( 2.0)
Macroscopic Resid 0 ( 0.0) 0 ( 0.0) 3 ( 0.5)
Not evaluable 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
No surg 115 (100.0) 7 ( 77.8) 0 ( 0.0)
Unknown 0 ( 0.0) 2 ( 22.2) 16 ( 2.9)
MARGINS_YN (%) No 0 ( 0.0) 0 ( 0.0) 511 ( 92.7) <0.001
Yes 0 ( 0.0) 0 ( 0.0) 24 ( 4.4)
No surg/Unk/NA 115 (100.0) 9 (100.0) 16 ( 2.9)
SURG_DISCHARGE_DAYS (mean (sd)) NaN (NA) NaN (NA) 1.00 (4.05) NA
READM_HOSP_30_DAYS_F (%) No_Surg_or_No_Readmit 109 ( 94.8) 9 (100.0) 509 ( 92.4) 0.156
Unplan_Readmit_Same 1 ( 0.9) 0 ( 0.0) 14 ( 2.5)
Plan_Readmit_Same 0 ( 0.0) 0 ( 0.0) 18 ( 3.3)
PlanUnplan_Same 1 ( 0.9) 0 ( 0.0) 0 ( 0.0)
9 4 ( 3.5) 0 ( 0.0) 10 ( 1.8)
RX_SUMM_RADIATION_F (%) None 94 ( 81.7) 8 ( 88.9) 363 ( 65.9) NaN
Beam Radiation 19 ( 16.5) 0 ( 0.0) 181 ( 32.8)
Radioactive Implants 0 ( 0.0) 0 ( 0.0) 2 ( 0.4)
Radioisotopes 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Beam + Imp or Isotopes 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Radiation, NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Unknown 2 ( 1.7) 1 ( 11.1) 5 ( 0.9)
PUF_30_DAY_MORT_CD_F (%) Alive_30 0 ( 0.0) 0 ( 0.0) 542 ( 98.4) <0.001
Dead_30 0 ( 0.0) 0 ( 0.0) 1 ( 0.2)
Unknown 0 ( 0.0) 0 ( 0.0) 6 ( 1.1)
NA 115 (100.0) 9 (100.0) 2 ( 0.4)
PUF_90_DAY_MORT_CD_F (%) Alive_90 0 ( 0.0) 0 ( 0.0) 536 ( 97.3) <0.001
Dead_90 0 ( 0.0) 0 ( 0.0) 3 ( 0.5)
Unknown 0 ( 0.0) 0 ( 0.0) 10 ( 1.8)
NA 115 (100.0) 9 (100.0) 2 ( 0.4)
DX_LASTCONTACT_DEATH_MONTHS (mean (sd)) 29.20 (33.73) 11.00 (11.63) 66.23 (41.60) <0.001
LYMPH_VASCULAR_INVASION_F (%) Neg_LymphVasc_Inv 12 ( 10.4) 3 ( 33.3) 147 ( 26.7) NaN
Pos_LumphVasc_Inv 0 ( 0.0) 0 ( 0.0) 10 ( 1.8)
N_A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Unknown 51 ( 44.3) 3 ( 33.3) 100 ( 18.1)
NA 52 ( 45.2) 3 ( 33.3) 294 ( 53.4)
RX_HOSP_SURG_APPR_2010_F (%) No_Surg 63 ( 54.8) 6 ( 66.7) 27 ( 4.9) NaN
Robot_Assist 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Robot_to_Open 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Endo_Lap 0 ( 0.0) 0 ( 0.0) 1 ( 0.2)
Endo_Lap_to_Open 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Open_Unknown 0 ( 0.0) 0 ( 0.0) 229 ( 41.6)
Unknown 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
NA 52 ( 45.2) 3 ( 33.3) 294 ( 53.4)
SURG_RAD_SEQ (%) Surg Alone 0 ( 0.0) 0 ( 0.0) 363 ( 65.9) NaN
Surg then Rad 0 ( 0.0) 0 ( 0.0) 181 ( 32.8)
Rad Alone 19 ( 16.5) 0 ( 0.0) 0 ( 0.0)
No Treatment 95 ( 82.6) 0 ( 0.0) 0 ( 0.0)
Other 1 ( 0.9) 9 (100.0) 5 ( 0.9)
Rad before and after Surg 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Rad then Surg 0 ( 0.0) 0 ( 0.0) 2 ( 0.4)
SURG_RAD_SEQ_C (%) Surg, No rad, No Chemo 0 ( 0.0) 0 ( 0.0) 321 ( 58.3) NaN
Surg then Rad, No Chemo 0 ( 0.0) 0 ( 0.0) 141 ( 25.6)
Surg then Rad, Yes Chemo 0 ( 0.0) 0 ( 0.0) 33 ( 6.0)
Surg, No rad, Yes Chemo 0 ( 0.0) 0 ( 0.0) 25 ( 4.5)
No Surg, No Rad, Yes Chemo 9 ( 7.8) 0 ( 0.0) 0 ( 0.0)
No Surg, No Rad, No Chemo 81 ( 70.4) 0 ( 0.0) 0 ( 0.0)
Other 8 ( 7.0) 9 (100.0) 29 ( 5.3)
Rad, No Surg, Yes Chemo 5 ( 4.3) 0 ( 0.0) 0 ( 0.0)
Rad, No Surg, No Chemo 12 ( 10.4) 0 ( 0.0) 0 ( 0.0)
Rad then Surg, Yes Chemo 0 ( 0.0) 0 ( 0.0) 2 ( 0.4)
Rad then Surg, No Chemo 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Rad before and after Surg, Yes Chemo 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Rad before and after Surg, No Chemo 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
T_SIZE (%) No Tumor 4 ( 3.5) 0 ( 0.0) 0 ( 0.0) <0.001
Microscopic focus 1 ( 0.9) 0 ( 0.0) 16 ( 2.9)
< 1 cm 12 ( 10.4) 2 ( 22.2) 70 ( 12.7)
1-2 cm 8 ( 7.0) 0 ( 0.0) 83 ( 15.1)
2-3 cm 7 ( 6.1) 1 ( 11.1) 45 ( 8.2)
3-4 cm 6 ( 5.2) 0 ( 0.0) 21 ( 3.8)
4-5 cm 5 ( 4.3) 0 ( 0.0) 6 ( 1.1)
5-6 cm 3 ( 2.6) 0 ( 0.0) 5 ( 0.9)
>6 cm 11 ( 9.6) 0 ( 0.0) 20 ( 3.6)
NA_unk 58 ( 50.4) 6 ( 66.7) 285 ( 51.7)
SURGERY_YN (%) No 115 (100.0) 0 ( 0.0) 0 ( 0.0) <0.001
Ukn 0 ( 0.0) 9 (100.0) 0 ( 0.0)
Yes 0 ( 0.0) 0 ( 0.0) 551 (100.0)
RADIATION_YN (%) No 95 ( 82.6) 8 ( 88.9) 363 ( 65.9) <0.001
Yes 19 ( 16.5) 0 ( 0.0) 183 ( 33.2)
NA 1 ( 0.9) 1 ( 11.1) 5 ( 0.9)
CHEMO_YN (%) No 93 ( 80.9) 7 ( 77.8) 463 ( 84.0) 0.175
Yes 14 ( 12.2) 0 ( 0.0) 60 ( 10.9)
Ukn 8 ( 7.0) 2 ( 22.2) 28 ( 5.1)
IMMUNO_YN (%) No 112 ( 97.4) 8 ( 88.9) 537 ( 97.5) 0.226
Yes 0 ( 0.0) 0 ( 0.0) 5 ( 0.9)
Ukn 3 ( 2.6) 1 ( 11.1) 9 ( 1.6)
Tx_YN (%) FALSE 80 ( 69.6) 0 ( 0.0) 0 ( 0.0) <0.001
TRUE 27 ( 23.5) 7 ( 77.8) 523 ( 94.9)
NA 8 ( 7.0) 2 ( 22.2) 28 ( 5.1)
mets_at_dx (%) Bone 9 ( 7.8) 0 ( 0.0) 1 ( 0.2) NaN
Brain 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Liver 0 ( 0.0) 0 ( 0.0) 1 ( 0.2)
Lung 4 ( 3.5) 0 ( 0.0) 0 ( 0.0)
None/Other/Unk/NA 102 ( 88.7) 9 (100.0) 549 ( 99.6)
MEDICAID_EXPN_CODE (%) Non-Expansion State 48 ( 41.7) 2 ( 22.2) 211 ( 38.3) 0.588
Jan 2014 Expansion States 38 ( 33.0) 3 ( 33.3) 166 ( 30.1)
Early Expansion States (2010-13) 12 ( 10.4) 2 ( 22.2) 63 ( 11.4)
Late Expansion States (> Jan 2014) 15 ( 13.0) 2 ( 22.2) 78 ( 14.2)
Suppressed for Ages 0 - 39 2 ( 1.7) 0 ( 0.0) 33 ( 6.0)

p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN", 
                 "CHEMO_YN", "IMMUNO_YN", "Tx_YN", "mets_at_dx",
                 "MEDICAID_EXPN_CODE"), 
        strata = "RADIATION_YN")

level No Yes p test
n 466 202
FACILITY_TYPE_F (%) Community Cancer Program 63 ( 13.5) 17 ( 8.4) 0.217
Comprehensive Comm Ca Program 202 ( 43.3) 89 ( 44.1)
Academic/Research Program 112 ( 24.0) 61 ( 30.2)
Integrated Network Ca Program 66 ( 14.2) 24 ( 11.9)
NA 23 ( 4.9) 11 ( 5.4)
FACILITY_LOCATION_F (%) New England 18 ( 3.9) 12 ( 5.9) 0.148
Middle Atlantic 65 ( 13.9) 27 ( 13.4)
South Atlantic 102 ( 21.9) 41 ( 20.3)
East North Central 101 ( 21.7) 33 ( 16.3)
East South Central 31 ( 6.7) 13 ( 6.4)
West North Central 42 ( 9.0) 17 ( 8.4)
West South Central 39 ( 8.4) 19 ( 9.4)
Mountain 15 ( 3.2) 18 ( 8.9)
Pacific 30 ( 6.4) 11 ( 5.4)
NA 23 ( 4.9) 11 ( 5.4)
FACILITY_GEOGRAPHY (%) Northeast 83 ( 17.8) 39 ( 19.3) 0.334
South 141 ( 30.3) 60 ( 29.7)
Midwest 174 ( 37.3) 63 ( 31.2)
West 45 ( 9.7) 29 ( 14.4)
NA 23 ( 4.9) 11 ( 5.4)
AGE (mean (sd)) 67.48 (15.44) 60.96 (12.94) <0.001
AGE_F (%) (0,54] 98 ( 21.0) 62 ( 30.7) <0.001
(54,64] 94 ( 20.2) 54 ( 26.7)
(64,74] 91 ( 19.5) 56 ( 27.7)
(74,100] 183 ( 39.3) 30 ( 14.9)
AGE_40 (%) (0,40] 23 ( 4.9) 14 ( 6.9) 0.395
(40,100] 443 ( 95.1) 188 ( 93.1)
SEX_F (%) Male 14 ( 3.0) 5 ( 2.5) 0.901
Female 452 ( 97.0) 197 ( 97.5)
RACE_F (%) White 398 ( 85.4) 167 ( 82.7) 0.846
Black 50 ( 10.7) 26 ( 12.9)
Other/Unk 10 ( 2.1) 5 ( 2.5)
Asian 8 ( 1.7) 4 ( 2.0)
HISPANIC (%) No 402 ( 86.3) 179 ( 88.6) 0.708
Yes 20 ( 4.3) 7 ( 3.5)
Unknown 44 ( 9.4) 16 ( 7.9)
INSURANCE_F (%) Private 168 ( 36.1) 103 ( 51.0) 0.002
None 16 ( 3.4) 8 ( 4.0)
Medicaid 25 ( 5.4) 10 ( 5.0)
Medicare 239 ( 51.3) 76 ( 37.6)
Other Government 4 ( 0.9) 4 ( 2.0)
Unknown 14 ( 3.0) 1 ( 0.5)
INCOME_F (%) Less than $38,000 94 ( 20.2) 35 ( 17.3) 0.166
$38,000 - $47,999 104 ( 22.3) 38 ( 18.8)
$48,000 - $62,999 128 ( 27.5) 48 ( 23.8)
$63,000 + 137 ( 29.4) 79 ( 39.1)
NA 3 ( 0.6) 2 ( 1.0)
EDUCATION_F (%) 21% or more 72 ( 15.5) 25 ( 12.4) 0.406
13 - 20.9% 119 ( 25.5) 45 ( 22.3)
7 - 12.9% 154 ( 33.0) 66 ( 32.7)
Less than 7% 118 ( 25.3) 65 ( 32.2)
NA 3 ( 0.6) 1 ( 0.5)
U_R_F (%) Metro 370 ( 79.4) 180 ( 89.1) 0.006
Urban 74 ( 15.9) 14 ( 6.9)
Rural 7 ( 1.5) 5 ( 2.5)
NA 15 ( 3.2) 3 ( 1.5)
CROWFLY (mean (sd)) 31.23 (135.66) 14.58 (21.82) 0.084
CDCC_TOTAL_BEST (%) 0 376 ( 80.7) 175 ( 86.6) 0.204
1 64 ( 13.7) 16 ( 7.9)
2 16 ( 3.4) 7 ( 3.5)
3 10 ( 2.1) 4 ( 2.0)
SITE_TEXT (%) C00.0 External Lip: Upper NOS 0 ( 0.0) 0 ( 0.0) NaN
C00.1 External Lip: Lower NOS 0 ( 0.0) 0 ( 0.0)
C00.2 External Lip: NOS 0 ( 0.0) 0 ( 0.0)
C00.3 Lip: Upper Mucosa 0 ( 0.0) 0 ( 0.0)
C00.4 Lip: Lower Mucosa 0 ( 0.0) 0 ( 0.0)
C00.5 Lip: Mucosa NOS 0 ( 0.0) 0 ( 0.0)
C00.6 Lip: Commissure 0 ( 0.0) 0 ( 0.0)
C00.8 Lip: Overlapping 0 ( 0.0) 0 ( 0.0)
C00.9 Lip NOS 0 ( 0.0) 0 ( 0.0)
C01.9 Tongue: Base NOS 0 ( 0.0) 0 ( 0.0)
C02.0 Tongue: Dorsal NOS 0 ( 0.0) 0 ( 0.0)
C02.1 Tongue: Border, Tip 0 ( 0.0) 0 ( 0.0)
C02.2 Tongue: Ventral NOS 0 ( 0.0) 0 ( 0.0)
C02.3 Tongue: Anterior NOS 0 ( 0.0) 0 ( 0.0)
C02.4 Lingual Tonsil 0 ( 0.0) 0 ( 0.0)
C02.8 Tongue: Overlapping 0 ( 0.0) 0 ( 0.0)
C02.9 Tongue: NOS 0 ( 0.0) 0 ( 0.0)
C03.0 Gum: Upper 0 ( 0.0) 0 ( 0.0)
C03.1 Gum: Lower 0 ( 0.0) 0 ( 0.0)
C03.9 Gum NOS 0 ( 0.0) 0 ( 0.0)
C04.0 Mouth: Anterior Floor 0 ( 0.0) 0 ( 0.0)
C04.1 Mouth: Lateral Floor 0 ( 0.0) 0 ( 0.0)
C04.9 Floor of Mouth NOS 0 ( 0.0) 0 ( 0.0)
C05.0 Hard Palate 0 ( 0.0) 0 ( 0.0)
C05.1 Soft Palate NOS 0 ( 0.0) 0 ( 0.0)
C05.2 Uvula 0 ( 0.0) 0 ( 0.0)
C05.8 Palate: Overlapping 0 ( 0.0) 0 ( 0.0)
C05.9 Palate NOS 0 ( 0.0) 0 ( 0.0)
C06.0 Cheek Mucosa 0 ( 0.0) 0 ( 0.0)
C06.1 Mouth: Vestibule 0 ( 0.0) 0 ( 0.0)
C06.2 Retromolar Area 0 ( 0.0) 0 ( 0.0)
C06.8 Mouth: Other Overlapping 0 ( 0.0) 0 ( 0.0)
C06.9 Mouth NOS 0 ( 0.0) 0 ( 0.0)
C07.9 Parotid Gland 0 ( 0.0) 0 ( 0.0)
C09.8 Tonsil: Overlapping 0 ( 0.0) 0 ( 0.0)
C09.9 Tonsil NOS 0 ( 0.0) 0 ( 0.0)
C11.1 Nasopharynx: Poster Wall 0 ( 0.0) 0 ( 0.0)
C14.2 Waldeyer Ring 0 ( 0.0) 0 ( 0.0)
C30.0 Nasal Cavity 0 ( 0.0) 0 ( 0.0)
C37.9 Thymus 0 ( 0.0) 0 ( 0.0)
C42.0 Blood 0 ( 0.0) 0 ( 0.0)
C42.2 Spleen 0 ( 0.0) 0 ( 0.0)
C42.4 Hematopoietic NOS 0 ( 0.0) 0 ( 0.0)
C44.0 Skin of lip, NOS 0 ( 0.0) 0 ( 0.0)
C44.1 Eyelid 0 ( 0.0) 0 ( 0.0)
C44.2 External ear 0 ( 0.0) 0 ( 0.0)
C44.3 Skin of ear and unspecified parts of face 0 ( 0.0) 0 ( 0.0)
C44.4 Skin of scalp and neck 0 ( 0.0) 0 ( 0.0)
C44.5 Skin of trunk 0 ( 0.0) 0 ( 0.0)
C44.6 Skin of upper limb and shoulder 0 ( 0.0) 0 ( 0.0)
C44.7 Skin of lower limb and hip 0 ( 0.0) 0 ( 0.0)
C44.8 Overlapping lesion of skin 0 ( 0.0) 0 ( 0.0)
C44.9 Skin, NOS 0 ( 0.0) 0 ( 0.0)
C50.0 Nipple 316 ( 67.8) 126 ( 62.4)
C51.0 Labium majus 0 ( 0.0) 0 ( 0.0)
C51.1 Labium minus 0 ( 0.0) 0 ( 0.0)
C51.2 Clitoris 0 ( 0.0) 0 ( 0.0)
C51.8 Overlapping lesion of vulva 0 ( 0.0) 0 ( 0.0)
C51.9 Vulva, NOS 0 ( 0.0) 0 ( 0.0)
C52.9 Vagina, NOS 0 ( 0.0) 0 ( 0.0)
C60.0 Prepuce 0 ( 0.0) 0 ( 0.0)
C60.1 Glans penis 0 ( 0.0) 0 ( 0.0)
C60.2 Body of penis 0 ( 0.0) 0 ( 0.0)
C60.8 Overlapping lesion of penis 0 ( 0.0) 0 ( 0.0)
C60.9 Penis 0 ( 0.0) 0 ( 0.0)
C63.2 Scrotum, NOS 0 ( 0.0) 0 ( 0.0)
C77.0 Lymph Nodes: HeadFaceNeck 0 ( 0.0) 0 ( 0.0)
C77.1 Intrathoracic Lymph Nodes 0 ( 0.0) 0 ( 0.0)
C77.2 Intra-abdominal LymphNodes 0 ( 0.0) 0 ( 0.0)
C77.3 Lymph Nodes of axilla or arm 0 ( 0.0) 0 ( 0.0)
C77.4 Lymph Nodes: Leg 0 ( 0.0) 0 ( 0.0)
C77.5 Pelvic Lymph Nodes 0 ( 0.0) 0 ( 0.0)
C77.8 Lymph Nodes: multiple region 0 ( 0.0) 0 ( 0.0)
C77.9 Lymph Node NOS 0 ( 0.0) 0 ( 0.0)
NA 150 ( 32.2) 76 ( 37.6)
BEHAVIOR (%) 2 0 ( 0.0) 0 ( 0.0) NaN
3 466 (100.0) 202 (100.0)
GRADE_F (%) Gr I: Well Diff 14 ( 3.0) 8 ( 4.0) NaN
Gr II: Mod Diff 36 ( 7.7) 19 ( 9.4)
Gr III: Poor Diff 66 ( 14.2) 38 ( 18.8)
Gr IV: Undiff/Anaplastic 2 ( 0.4) 0 ( 0.0)
5 0 ( 0.0) 0 ( 0.0)
6 0 ( 0.0) 0 ( 0.0)
7 0 ( 0.0) 0 ( 0.0)
8 0 ( 0.0) 0 ( 0.0)
NA/Unkown 348 ( 74.7) 137 ( 67.8)
DX_STAGING_PROC_DAYS (mean (sd)) 1.54 (10.16) 1.99 (10.52) 0.654
TNM_CLIN_T (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
c0 3 ( 0.6) 2 ( 1.0)
c1 25 ( 5.4) 12 ( 5.9)
c1A 6 ( 1.3) 6 ( 3.0)
c1B 7 ( 1.5) 2 ( 1.0)
c1C 8 ( 1.7) 7 ( 3.5)
c1MI 3 ( 0.6) 1 ( 0.5)
c2 14 ( 3.0) 7 ( 3.5)
c2A 0 ( 0.0) 0 ( 0.0)
c2B 0 ( 0.0) 0 ( 0.0)
c2C 0 ( 0.0) 0 ( 0.0)
c2D 0 ( 0.0) 0 ( 0.0)
c3 11 ( 2.4) 7 ( 3.5)
c3A 0 ( 0.0) 0 ( 0.0)
c3B 0 ( 0.0) 0 ( 0.0)
c4 7 ( 1.5) 3 ( 1.5)
c4A 0 ( 0.0) 0 ( 0.0)
c4B 7 ( 1.5) 3 ( 1.5)
c4C 0 ( 0.0) 0 ( 0.0)
c4D 3 ( 0.6) 0 ( 0.0)
cX 136 ( 29.2) 51 ( 25.2)
pA 0 ( 0.0) 0 ( 0.0)
pIS 210 ( 45.1) 92 ( 45.5)
NA 26 ( 5.6) 9 ( 4.5)
TNM_CLIN_N (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
c0 312 ( 67.0) 132 ( 65.3)
c1 12 ( 2.6) 14 ( 6.9)
c1A 0 ( 0.0) 0 ( 0.0)
c1B 0 ( 0.0) 0 ( 0.0)
c2 3 ( 0.6) 3 ( 1.5)
c2A 0 ( 0.0) 2 ( 1.0)
c2B 1 ( 0.2) 0 ( 0.0)
c2C 0 ( 0.0) 0 ( 0.0)
c3 3 ( 0.6) 0 ( 0.0)
c3A 0 ( 0.0) 1 ( 0.5)
c3B 0 ( 0.0) 0 ( 0.0)
c3C 0 ( 0.0) 0 ( 0.0)
c4 0 ( 0.0) 0 ( 0.0)
cX 116 ( 24.9) 44 ( 21.8)
NA 19 ( 4.1) 6 ( 3.0)
TNM_CLIN_M (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
c0 424 ( 91.0) 184 ( 91.1)
c0I+ 0 ( 0.0) 0 ( 0.0)
c1 16 ( 3.4) 10 ( 5.0)
c1A 0 ( 0.0) 0 ( 0.0)
c1B 0 ( 0.0) 0 ( 0.0)
c1C 0 ( 0.0) 0 ( 0.0)
NA 26 ( 5.6) 8 ( 4.0)
TNM_CLIN_STAGE_GROUP (%) 0 235 ( 50.4) 93 ( 46.0) NaN
1 29 ( 6.2) 10 ( 5.0)
1A 18 ( 3.9) 14 ( 6.9)
1B 0 ( 0.0) 1 ( 0.5)
1C 0 ( 0.0) 0 ( 0.0)
2 0 ( 0.0) 0 ( 0.0)
2A 15 ( 3.2) 8 ( 4.0)
2B 10 ( 2.1) 3 ( 1.5)
2C 0 ( 0.0) 0 ( 0.0)
3 2 ( 0.4) 0 ( 0.0)
3A 3 ( 0.6) 4 ( 2.0)
3B 7 ( 1.5) 5 ( 2.5)
3C 1 ( 0.2) 2 ( 1.0)
4 17 ( 3.6) 10 ( 5.0)
4A 0 ( 0.0) 0 ( 0.0)
4A1 0 ( 0.0) 0 ( 0.0)
4A2 0 ( 0.0) 0 ( 0.0)
4B 0 ( 0.0) 0 ( 0.0)
4C 0 ( 0.0) 0 ( 0.0)
N_A 0 ( 0.0) 0 ( 0.0)
99 129 ( 27.7) 52 ( 25.7)
TNM_PATH_T (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
p0 4 ( 0.9) 4 ( 2.0)
p1 5 ( 1.1) 8 ( 4.0)
p1A 10 ( 2.1) 7 ( 3.5)
p1B 4 ( 0.9) 7 ( 3.5)
p1C 12 ( 2.6) 12 ( 5.9)
p1MI 6 ( 1.3) 1 ( 0.5)
p2 7 ( 1.5) 5 ( 2.5)
p2A 0 ( 0.0) 0 ( 0.0)
p2B 0 ( 0.0) 0 ( 0.0)
p2C 0 ( 0.0) 0 ( 0.0)
p2D 0 ( 0.0) 0 ( 0.0)
p3 1 ( 0.2) 1 ( 0.5)
p3A 0 ( 0.0) 0 ( 0.0)
p3B 0 ( 0.0) 0 ( 0.0)
p4 1 ( 0.2) 0 ( 0.0)
p4A 0 ( 0.0) 0 ( 0.0)
p4B 5 ( 1.1) 0 ( 0.0)
p4C 0 ( 0.0) 0 ( 0.0)
p4D 2 ( 0.4) 1 ( 0.5)
pA 0 ( 0.0) 0 ( 0.0)
pIS 174 ( 37.3) 82 ( 40.6)
pX 190 ( 40.8) 62 ( 30.7)
NA 45 ( 9.7) 12 ( 5.9)
TNM_PATH_N (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
p0 151 ( 32.4) 79 ( 39.1)
p0I- 18 ( 3.9) 12 ( 5.9)
p0I+ 2 ( 0.4) 0 ( 0.0)
p0M- 0 ( 0.0) 0 ( 0.0)
p0M+ 0 ( 0.0) 0 ( 0.0)
p1 3 ( 0.6) 4 ( 2.0)
p1A 4 ( 0.9) 5 ( 2.5)
p1B 0 ( 0.0) 0 ( 0.0)
p1C 0 ( 0.0) 0 ( 0.0)
p1MI 0 ( 0.0) 2 ( 1.0)
p2 0 ( 0.0) 2 ( 1.0)
p2A 1 ( 0.2) 1 ( 0.5)
p2B 0 ( 0.0) 0 ( 0.0)
p2C 0 ( 0.0) 0 ( 0.0)
p3 2 ( 0.4) 0 ( 0.0)
p3A 1 ( 0.2) 2 ( 1.0)
p3B 0 ( 0.0) 0 ( 0.0)
p3C 0 ( 0.0) 0 ( 0.0)
p4 0 ( 0.0) 0 ( 0.0)
pX 222 ( 47.6) 79 ( 39.1)
NA 62 ( 13.3) 16 ( 7.9)
TNM_PATH_M (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
p0 0 ( 0.0) 0 ( 0.0)
p1 6 ( 1.3) 2 ( 1.0)
p1A 0 ( 0.0) 0 ( 0.0)
p1B 0 ( 0.0) 0 ( 0.0)
p1C 0 ( 0.0) 0 ( 0.0)
pX 238 ( 51.1) 105 ( 52.0)
NA 222 ( 47.6) 95 ( 47.0)
TNM_PATH_STAGE_GROUP (%) 0 208 ( 44.6) 86 ( 42.6) NaN
1 36 ( 7.7) 19 ( 9.4)
1A 14 ( 3.0) 12 ( 5.9)
1B 0 ( 0.0) 2 ( 1.0)
1C 0 ( 0.0) 0 ( 0.0)
2 2 ( 0.4) 1 ( 0.5)
2A 10 ( 2.1) 11 ( 5.4)
2B 5 ( 1.1) 3 ( 1.5)
2C 0 ( 0.0) 0 ( 0.0)
3 0 ( 0.0) 0 ( 0.0)
3A 0 ( 0.0) 4 ( 2.0)
3B 5 ( 1.1) 1 ( 0.5)
3C 3 ( 0.6) 2 ( 1.0)
4 6 ( 1.3) 3 ( 1.5)
4A 0 ( 0.0) 0 ( 0.0)
4A1 0 ( 0.0) 0 ( 0.0)
4B 0 ( 0.0) 0 ( 0.0)
4C 0 ( 0.0) 0 ( 0.0)
N_A 0 ( 0.0) 0 ( 0.0)
99 145 ( 31.1) 50 ( 24.8)
NA 32 ( 6.9) 8 ( 4.0)
DX_RX_STARTED_DAYS (mean (sd)) 34.96 (36.22) 36.30 (32.06) 0.659
DX_SURG_STARTED_DAYS (mean (sd)) 35.59 (35.24) 45.29 (47.87) 0.008
DX_DEFSURG_STARTED_DAYS (mean (sd)) 43.22 (43.37) 53.04 (50.87) 0.020
MARGINS (%) No Residual 341 ( 73.2) 165 ( 81.7) NaN
Residual, NOS 5 ( 1.1) 5 ( 2.5)
Microscopic Resid 4 ( 0.9) 7 ( 3.5)
Macroscopic Resid 2 ( 0.4) 1 ( 0.5)
Not evaluable 0 ( 0.0) 0 ( 0.0)
No surg 102 ( 21.9) 19 ( 9.4)
Unknown 12 ( 2.6) 5 ( 2.5)
MARGINS_YN (%) No 341 ( 73.2) 165 ( 81.7) <0.001
Yes 11 ( 2.4) 13 ( 6.4)
No surg/Unk/NA 114 ( 24.5) 24 ( 11.9)
SURG_DISCHARGE_DAYS (mean (sd)) 0.99 (1.60) 1.06 (6.83) 0.859
READM_HOSP_30_DAYS_F (%) No_Surg_or_No_Readmit 434 ( 93.1) 188 ( 93.1) 0.343
Unplan_Readmit_Same 12 ( 2.6) 2 ( 1.0)
Plan_Readmit_Same 12 ( 2.6) 6 ( 3.0)
PlanUnplan_Same 0 ( 0.0) 1 ( 0.5)
9 8 ( 1.7) 5 ( 2.5)
RX_SUMM_RADIATION_F (%) None 465 ( 99.8) 0 ( 0.0) NaN
Beam Radiation 0 ( 0.0) 200 ( 99.0)
Radioactive Implants 0 ( 0.0) 2 ( 1.0)
Radioisotopes 0 ( 0.0) 0 ( 0.0)
Beam + Imp or Isotopes 0 ( 0.0) 0 ( 0.0)
Radiation, NOS 0 ( 0.0) 0 ( 0.0)
Unknown 1 ( 0.2) 0 ( 0.0)
PUF_30_DAY_MORT_CD_F (%) Alive_30 356 ( 76.4) 181 ( 89.6) 0.001
Dead_30 1 ( 0.2) 0 ( 0.0)
Unknown 4 ( 0.9) 2 ( 1.0)
NA 105 ( 22.5) 19 ( 9.4)
PUF_90_DAY_MORT_CD_F (%) Alive_90 351 ( 75.3) 180 ( 89.1) <0.001
Dead_90 3 ( 0.6) 0 ( 0.0)
Unknown 7 ( 1.5) 3 ( 1.5)
NA 105 ( 22.5) 19 ( 9.4)
DX_LASTCONTACT_DEATH_MONTHS (mean (sd)) 56.30 (41.93) 66.16 (43.99) 0.006
LYMPH_VASCULAR_INVASION_F (%) Neg_LymphVasc_Inv 110 ( 23.6) 49 ( 24.3) NaN
Pos_LumphVasc_Inv 4 ( 0.9) 6 ( 3.0)
N_A 0 ( 0.0) 0 ( 0.0)
Unknown 111 ( 23.8) 40 ( 19.8)
NA 241 ( 51.7) 107 ( 53.0)
RX_HOSP_SURG_APPR_2010_F (%) No_Surg 66 ( 14.2) 28 ( 13.9) NaN
Robot_Assist 0 ( 0.0) 0 ( 0.0)
Robot_to_Open 0 ( 0.0) 0 ( 0.0)
Endo_Lap 1 ( 0.2) 0 ( 0.0)
Endo_Lap_to_Open 0 ( 0.0) 0 ( 0.0)
Open_Unknown 158 ( 33.9) 67 ( 33.2)
Unknown 0 ( 0.0) 0 ( 0.0)
NA 241 ( 51.7) 107 ( 53.0)
SURG_RAD_SEQ (%) Surg Alone 363 ( 77.9) 0 ( 0.0) NaN
Surg then Rad 0 ( 0.0) 181 ( 89.6)
Rad Alone 0 ( 0.0) 19 ( 9.4)
No Treatment 95 ( 20.4) 0 ( 0.0)
Other 8 ( 1.7) 0 ( 0.0)
Rad before and after Surg 0 ( 0.0) 0 ( 0.0)
Rad then Surg 0 ( 0.0) 2 ( 1.0)
SURG_RAD_SEQ_C (%) Surg, No rad, No Chemo 321 ( 68.9) 0 ( 0.0) NaN
Surg then Rad, No Chemo 0 ( 0.0) 141 ( 69.8)
Surg then Rad, Yes Chemo 0 ( 0.0) 33 ( 16.3)
Surg, No rad, Yes Chemo 25 ( 5.4) 0 ( 0.0)
No Surg, No Rad, Yes Chemo 9 ( 1.9) 0 ( 0.0)
No Surg, No Rad, No Chemo 81 ( 17.4) 0 ( 0.0)
Other 30 ( 6.4) 9 ( 4.5)
Rad, No Surg, Yes Chemo 0 ( 0.0) 5 ( 2.5)
Rad, No Surg, No Chemo 0 ( 0.0) 12 ( 5.9)
Rad then Surg, Yes Chemo 0 ( 0.0) 2 ( 1.0)
Rad then Surg, No Chemo 0 ( 0.0) 0 ( 0.0)
Rad before and after Surg, Yes Chemo 0 ( 0.0) 0 ( 0.0)
Rad before and after Surg, No Chemo 0 ( 0.0) 0 ( 0.0)
T_SIZE (%) No Tumor 2 ( 0.4) 2 ( 1.0) 0.012
Microscopic focus 8 ( 1.7) 9 ( 4.5)
< 1 cm 57 ( 12.2) 25 ( 12.4)
1-2 cm 49 ( 10.5) 41 ( 20.3)
2-3 cm 37 ( 7.9) 16 ( 7.9)
3-4 cm 20 ( 4.3) 7 ( 3.5)
4-5 cm 9 ( 1.9) 2 ( 1.0)
5-6 cm 5 ( 1.1) 3 ( 1.5)
>6 cm 20 ( 4.3) 11 ( 5.4)
NA_unk 259 ( 55.6) 86 ( 42.6)
SURGERY_YN (%) No 95 ( 20.4) 19 ( 9.4) <0.001
Ukn 8 ( 1.7) 0 ( 0.0)
Yes 363 ( 77.9) 183 ( 90.6)
RADIATION_YN (%) No 466 (100.0) 0 ( 0.0) NaN
Yes 0 ( 0.0) 202 (100.0)
NA 0 ( 0.0) 0 ( 0.0)
CHEMO_YN (%) No 409 ( 87.8) 153 ( 75.7) <0.001
Yes 34 ( 7.3) 40 ( 19.8)
Ukn 23 ( 4.9) 9 ( 4.5)
IMMUNO_YN (%) No 457 ( 98.1) 197 ( 97.5) 0.873
Yes 3 ( 0.6) 2 ( 1.0)
Ukn 6 ( 1.3) 3 ( 1.5)
Tx_YN (%) FALSE 80 ( 17.2) 0 ( 0.0) <0.001
TRUE 363 ( 77.9) 193 ( 95.5)
NA 23 ( 4.9) 9 ( 4.5)
mets_at_dx (%) Bone 8 ( 1.7) 2 ( 1.0) NaN
Brain 0 ( 0.0) 0 ( 0.0)
Liver 1 ( 0.2) 0 ( 0.0)
Lung 1 ( 0.2) 3 ( 1.5)
None/Other/Unk/NA 456 ( 97.9) 197 ( 97.5)
MEDICAID_EXPN_CODE (%) Non-Expansion State 189 ( 40.6) 71 ( 35.1) 0.605
Jan 2014 Expansion States 138 ( 29.6) 67 ( 33.2)
Early Expansion States (2010-13) 55 ( 11.8) 21 ( 10.4)
Late Expansion States (> Jan 2014) 61 ( 13.1) 32 ( 15.8)
Suppressed for Ages 0 - 39 23 ( 4.9) 11 ( 5.4)

p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN", 
                 "CHEMO_YN", "IMMUNO_YN", "Tx_YN","mets_at_dx",
                 "MEDICAID_EXPN_CODE"), 
        strata = "CHEMO_YN")

level No Yes Ukn p test
n 563 74 38
FACILITY_TYPE_F (%) Community Cancer Program 71 ( 12.6) 7 ( 9.5) 3 ( 7.9) <0.001
Comprehensive Comm Ca Program 244 ( 43.3) 32 ( 43.2) 20 ( 52.6)
Academic/Research Program 154 ( 27.4) 13 ( 17.6) 6 ( 15.8)
Integrated Network Ca Program 76 ( 13.5) 9 ( 12.2) 5 ( 13.2)
NA 18 ( 3.2) 13 ( 17.6) 4 ( 10.5)
FACILITY_LOCATION_F (%) New England 25 ( 4.4) 5 ( 6.8) 1 ( 2.6) 0.002
Middle Atlantic 78 ( 13.9) 6 ( 8.1) 9 ( 23.7)
South Atlantic 124 ( 22.0) 13 ( 17.6) 6 ( 15.8)
East North Central 117 ( 20.8) 12 ( 16.2) 5 ( 13.2)
East South Central 39 ( 6.9) 4 ( 5.4) 1 ( 2.6)
West North Central 50 ( 8.9) 6 ( 8.1) 3 ( 7.9)
West South Central 46 ( 8.2) 9 ( 12.2) 5 ( 13.2)
Mountain 30 ( 5.3) 3 ( 4.1) 2 ( 5.3)
Pacific 36 ( 6.4) 3 ( 4.1) 2 ( 5.3)
NA 18 ( 3.2) 13 ( 17.6) 4 ( 10.5)
FACILITY_GEOGRAPHY (%) Northeast 103 ( 18.3) 11 ( 14.9) 10 ( 26.3) <0.001
South 170 ( 30.2) 22 ( 29.7) 11 ( 28.9)
Midwest 206 ( 36.6) 22 ( 29.7) 9 ( 23.7)
West 66 ( 11.7) 6 ( 8.1) 4 ( 10.5)
NA 18 ( 3.2) 13 ( 17.6) 4 ( 10.5)
AGE (mean (sd)) 67.39 (14.49) 52.42 (12.10) 64.11 (15.57) <0.001
AGE_F (%) (0,54] 107 ( 19.0) 42 ( 56.8) 12 ( 31.6) <0.001
(54,64] 126 ( 22.4) 19 ( 25.7) 4 ( 10.5)
(64,74] 128 ( 22.7) 11 ( 14.9) 10 ( 26.3)
(74,100] 202 ( 35.9) 2 ( 2.7) 12 ( 31.6)
AGE_40 (%) (0,40] 20 ( 3.6) 13 ( 17.6) 5 ( 13.2) <0.001
(40,100] 543 ( 96.4) 61 ( 82.4) 33 ( 86.8)
SEX_F (%) Male 15 ( 2.7) 3 ( 4.1) 1 ( 2.6) 0.792
Female 548 ( 97.3) 71 ( 95.9) 37 ( 97.4)
RACE_F (%) White 487 ( 86.5) 55 ( 74.3) 28 ( 73.7) 0.036
Black 54 ( 9.6) 16 ( 21.6) 7 ( 18.4)
Other/Unk 12 ( 2.1) 2 ( 2.7) 2 ( 5.3)
Asian 10 ( 1.8) 1 ( 1.4) 1 ( 2.6)
HISPANIC (%) No 494 ( 87.7) 62 ( 83.8) 31 ( 81.6) 0.576
Yes 20 ( 3.6) 5 ( 6.8) 2 ( 5.3)
Unknown 49 ( 8.7) 7 ( 9.5) 5 ( 13.2)
INSURANCE_F (%) Private 212 ( 37.7) 47 ( 63.5) 14 ( 36.8) <0.001
None 19 ( 3.4) 3 ( 4.1) 2 ( 5.3)
Medicaid 25 ( 4.4) 8 ( 10.8) 2 ( 5.3)
Medicare 288 ( 51.2) 13 ( 17.6) 19 ( 50.0)
Other Government 6 ( 1.1) 2 ( 2.7) 0 ( 0.0)
Unknown 13 ( 2.3) 1 ( 1.4) 1 ( 2.6)
INCOME_F (%) Less than $38,000 106 ( 18.8) 16 ( 21.6) 8 ( 21.1) 0.455
$38,000 - $47,999 119 ( 21.1) 16 ( 21.6) 9 ( 23.7)
$48,000 - $62,999 155 ( 27.5) 17 ( 23.0) 6 ( 15.8)
$63,000 + 180 ( 32.0) 23 ( 31.1) 15 ( 39.5)
NA 3 ( 0.5) 2 ( 2.7) 0 ( 0.0)
EDUCATION_F (%) 21% or more 75 ( 13.3) 15 ( 20.3) 9 ( 23.7) 0.412
13 - 20.9% 140 ( 24.9) 14 ( 18.9) 10 ( 26.3)
7 - 12.9% 189 ( 33.6) 25 ( 33.8) 8 ( 21.1)
Less than 7% 156 ( 27.7) 19 ( 25.7) 11 ( 28.9)
NA 3 ( 0.5) 1 ( 1.4) 0 ( 0.0)
U_R_F (%) Metro 464 ( 82.4) 60 ( 81.1) 33 ( 86.8) 0.561
Urban 73 ( 13.0) 12 ( 16.2) 3 ( 7.9)
Rural 12 ( 2.1) 0 ( 0.0) 0 ( 0.0)
NA 14 ( 2.5) 2 ( 2.7) 2 ( 5.3)
CROWFLY (mean (sd)) 24.76 (94.40) 17.73 (21.82) 59.73 (310.83) 0.149
CDCC_TOTAL_BEST (%) 0 462 ( 82.1) 65 ( 87.8) 31 ( 81.6) 0.535
1 67 ( 11.9) 7 ( 9.5) 6 ( 15.8)
2 22 ( 3.9) 0 ( 0.0) 1 ( 2.6)
3 12 ( 2.1) 2 ( 2.7) 0 ( 0.0)
SITE_TEXT (%) C00.0 External Lip: Upper NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) NaN
C00.1 External Lip: Lower NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.2 External Lip: NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.3 Lip: Upper Mucosa 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.4 Lip: Lower Mucosa 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.5 Lip: Mucosa NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.6 Lip: Commissure 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.8 Lip: Overlapping 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.9 Lip NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C01.9 Tongue: Base NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.0 Tongue: Dorsal NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.1 Tongue: Border, Tip 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.2 Tongue: Ventral NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.3 Tongue: Anterior NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.4 Lingual Tonsil 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.8 Tongue: Overlapping 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.9 Tongue: NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C03.0 Gum: Upper 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C03.1 Gum: Lower 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C03.9 Gum NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C04.0 Mouth: Anterior Floor 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C04.1 Mouth: Lateral Floor 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C04.9 Floor of Mouth NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C05.0 Hard Palate 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C05.1 Soft Palate NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C05.2 Uvula 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C05.8 Palate: Overlapping 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C05.9 Palate NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C06.0 Cheek Mucosa 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C06.1 Mouth: Vestibule 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C06.2 Retromolar Area 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C06.8 Mouth: Other Overlapping 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C06.9 Mouth NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C07.9 Parotid Gland 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C09.8 Tonsil: Overlapping 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C09.9 Tonsil NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C11.1 Nasopharynx: Poster Wall 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C14.2 Waldeyer Ring 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C30.0 Nasal Cavity 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C37.9 Thymus 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C42.0 Blood 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C42.2 Spleen 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C42.4 Hematopoietic NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.0 Skin of lip, NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.1 Eyelid 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.2 External ear 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.3 Skin of ear and unspecified parts of face 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.4 Skin of scalp and neck 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.5 Skin of trunk 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.6 Skin of upper limb and shoulder 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.7 Skin of lower limb and hip 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.8 Overlapping lesion of skin 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.9 Skin, NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C50.0 Nipple 415 ( 73.7) 8 ( 10.8) 25 ( 65.8)
C51.0 Labium majus 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C51.1 Labium minus 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C51.2 Clitoris 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C51.8 Overlapping lesion of vulva 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C51.9 Vulva, NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C52.9 Vagina, NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C60.0 Prepuce 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C60.1 Glans penis 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C60.2 Body of penis 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C60.8 Overlapping lesion of penis 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C60.9 Penis 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C63.2 Scrotum, NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.0 Lymph Nodes: HeadFaceNeck 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.1 Intrathoracic Lymph Nodes 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.2 Intra-abdominal LymphNodes 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.3 Lymph Nodes of axilla or arm 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.4 Lymph Nodes: Leg 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.5 Pelvic Lymph Nodes 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.8 Lymph Nodes: multiple region 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.9 Lymph Node NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
NA 148 ( 26.3) 66 ( 89.2) 13 ( 34.2)
BEHAVIOR (%) 2 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) NaN
3 563 (100.0) 74 (100.0) 38 (100.0)
GRADE_F (%) Gr I: Well Diff 18 ( 3.2) 3 ( 4.1) 1 ( 2.6) NaN
Gr II: Mod Diff 38 ( 6.7) 16 ( 21.6) 1 ( 2.6)
Gr III: Poor Diff 58 ( 10.3) 38 ( 51.4) 10 ( 26.3)
Gr IV: Undiff/Anaplastic 2 ( 0.4) 0 ( 0.0) 0 ( 0.0)
5 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
6 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
7 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
8 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
NA/Unkown 447 ( 79.4) 17 ( 23.0) 26 ( 68.4)
DX_STAGING_PROC_DAYS (mean (sd)) 1.10 (7.32) 6.38 (22.02) 0.00 (0.00) 0.001
TNM_CLIN_T (%) N_A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) NaN
c0 4 ( 0.7) 1 ( 1.4) 0 ( 0.0)
c1 27 ( 4.8) 7 ( 9.5) 3 ( 7.9)
c1A 10 ( 1.8) 2 ( 2.7) 0 ( 0.0)
c1B 8 ( 1.4) 0 ( 0.0) 1 ( 2.6)
c1C 12 ( 2.1) 3 ( 4.1) 0 ( 0.0)
c1MI 4 ( 0.7) 0 ( 0.0) 0 ( 0.0)
c2 13 ( 2.3) 8 ( 10.8) 0 ( 0.0)
c2A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c2B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c2C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c2D 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c3 6 ( 1.1) 12 ( 16.2) 0 ( 0.0)
c3A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c3B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c4 5 ( 0.9) 5 ( 6.8) 0 ( 0.0)
c4A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c4B 6 ( 1.1) 3 ( 4.1) 1 ( 2.6)
c4C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c4D 1 ( 0.2) 2 ( 2.7) 0 ( 0.0)
cX 146 ( 25.9) 28 ( 37.8) 14 ( 36.8)
pA 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
pIS 286 ( 50.8) 3 ( 4.1) 18 ( 47.4)
NA 35 ( 6.2) 0 ( 0.0) 1 ( 2.6)
TNM_CLIN_N (%) N_A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) NaN
c0 399 ( 70.9) 30 ( 40.5) 20 ( 52.6)
c1 14 ( 2.5) 12 ( 16.2) 0 ( 0.0)
c1A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c1B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c2 3 ( 0.5) 1 ( 1.4) 2 ( 5.3)
c2A 1 ( 0.2) 1 ( 1.4) 0 ( 0.0)
c2B 1 ( 0.2) 0 ( 0.0) 0 ( 0.0)
c2C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c3 1 ( 0.2) 2 ( 2.7) 0 ( 0.0)
c3A 0 ( 0.0) 1 ( 1.4) 0 ( 0.0)
c3B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c3C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c4 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
cX 120 ( 21.3) 26 ( 35.1) 16 ( 42.1)
NA 24 ( 4.3) 1 ( 1.4) 0 ( 0.0)
TNM_CLIN_M (%) N_A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) NaN
c0 519 ( 92.2) 62 ( 83.8) 33 ( 86.8)
c0I+ 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c1 13 ( 2.3) 11 ( 14.9) 2 ( 5.3)
c1A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c1B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
NA 31 ( 5.5) 1 ( 1.4) 3 ( 7.9)
TNM_CLIN_STAGE_GROUP (%) 0 312 ( 55.4) 3 ( 4.1) 18 ( 47.4) NaN
1 33 ( 5.9) 1 ( 1.4) 5 ( 13.2)
1A 25 ( 4.4) 7 ( 9.5) 0 ( 0.0)
1B 1 ( 0.2) 0 ( 0.0) 0 ( 0.0)
1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
2 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
2A 13 ( 2.3) 10 ( 13.5) 0 ( 0.0)
2B 7 ( 1.2) 6 ( 8.1) 0 ( 0.0)
2C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
3 1 ( 0.2) 1 ( 1.4) 0 ( 0.0)
3A 3 ( 0.5) 4 ( 5.4) 0 ( 0.0)
3B 6 ( 1.1) 6 ( 8.1) 0 ( 0.0)
3C 1 ( 0.2) 2 ( 2.7) 0 ( 0.0)
4 14 ( 2.5) 11 ( 14.9) 2 ( 5.3)
4A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
4A1 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
4A2 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
4B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
4C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
N_A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
99 147 ( 26.1) 23 ( 31.1) 13 ( 34.2)
TNM_PATH_T (%) N_A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) NaN
p0 5 ( 0.9) 3 ( 4.1) 0 ( 0.0)
p1 11 ( 2.0) 1 ( 1.4) 1 ( 2.6)
p1A 15 ( 2.7) 2 ( 2.7) 0 ( 0.0)
p1B 4 ( 0.7) 6 ( 8.1) 1 ( 2.6)
p1C 13 ( 2.3) 10 ( 13.5) 1 ( 2.6)
p1MI 6 ( 1.1) 1 ( 1.4) 0 ( 0.0)
p2 5 ( 0.9) 7 ( 9.5) 0 ( 0.0)
p2A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p2B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p2C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p2D 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p3 0 ( 0.0) 2 ( 2.7) 0 ( 0.0)
p3A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p3B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p4 1 ( 0.2) 0 ( 0.0) 0 ( 0.0)
p4A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p4B 3 ( 0.5) 2 ( 2.7) 0 ( 0.0)
p4C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p4D 1 ( 0.2) 2 ( 2.7) 0 ( 0.0)
pA 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
pIS 241 ( 42.8) 2 ( 2.7) 18 ( 47.4)
pX 208 ( 36.9) 32 ( 43.2) 13 ( 34.2)
NA 50 ( 8.9) 4 ( 5.4) 4 ( 10.5)
TNM_PATH_N (%) N_A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) NaN
p0 201 ( 35.7) 17 ( 23.0) 15 ( 39.5)
p0I- 24 ( 4.3) 5 ( 6.8) 1 ( 2.6)
p0I+ 1 ( 0.2) 1 ( 1.4) 0 ( 0.0)
p0M- 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p0M+ 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1 0 ( 0.0) 7 ( 9.5) 0 ( 0.0)
p1A 3 ( 0.5) 6 ( 8.1) 0 ( 0.0)
p1B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1MI 2 ( 0.4) 0 ( 0.0) 0 ( 0.0)
p2 0 ( 0.0) 2 ( 2.7) 0 ( 0.0)
p2A 2 ( 0.4) 0 ( 0.0) 0 ( 0.0)
p2B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p2C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p3 1 ( 0.2) 1 ( 1.4) 0 ( 0.0)
p3A 0 ( 0.0) 3 ( 4.1) 0 ( 0.0)
p3B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p3C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p4 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
pX 258 ( 45.8) 28 ( 37.8) 18 ( 47.4)
NA 71 ( 12.6) 4 ( 5.4) 4 ( 10.5)
TNM_PATH_M (%) N_A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) NaN
p0 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1 2 ( 0.4) 6 ( 8.1) 0 ( 0.0)
p1A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
pX 285 ( 50.6) 37 ( 50.0) 22 ( 57.9)
NA 276 ( 49.0) 31 ( 41.9) 16 ( 42.1)
TNM_PATH_STAGE_GROUP (%) 0 279 ( 49.6) 2 ( 2.7) 17 ( 44.7) NaN
1 45 ( 8.0) 6 ( 8.1) 4 ( 10.5)
1A 18 ( 3.2) 8 ( 10.8) 0 ( 0.0)
1B 1 ( 0.2) 1 ( 1.4) 0 ( 0.0)
1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
2 1 ( 0.2) 2 ( 2.7) 0 ( 0.0)
2A 11 ( 2.0) 10 ( 13.5) 0 ( 0.0)
2B 2 ( 0.4) 6 ( 8.1) 0 ( 0.0)
2C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
3 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
3A 1 ( 0.2) 3 ( 4.1) 0 ( 0.0)
3B 4 ( 0.7) 2 ( 2.7) 0 ( 0.0)
3C 2 ( 0.4) 3 ( 4.1) 0 ( 0.0)
4 2 ( 0.4) 7 ( 9.5) 0 ( 0.0)
4A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
4A1 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
4B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
4C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
N_A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
99 164 ( 29.1) 20 ( 27.0) 14 ( 36.8)
NA 33 ( 5.9) 4 ( 5.4) 3 ( 7.9)
DX_RX_STARTED_DAYS (mean (sd)) 36.02 (35.73) 34.75 (27.98) 27.34 (35.79) 0.423
DX_SURG_STARTED_DAYS (mean (sd)) 34.31 (29.50) 80.57 (75.72) 22.48 (25.84) <0.001
DX_DEFSURG_STARTED_DAYS (mean (sd)) 40.62 (37.04) 96.98 (75.88) 33.67 (24.15) <0.001
MARGINS (%) No Residual 434 ( 77.1) 50 ( 67.6) 27 ( 71.1) NaN
Residual, NOS 8 ( 1.4) 2 ( 2.7) 0 ( 0.0)
Microscopic Resid 6 ( 1.1) 5 ( 6.8) 0 ( 0.0)
Macroscopic Resid 1 ( 0.2) 1 ( 1.4) 1 ( 2.6)
Not evaluable 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
No surg 99 ( 17.6) 14 ( 18.9) 9 ( 23.7)
Unknown 15 ( 2.7) 2 ( 2.7) 1 ( 2.6)
MARGINS_YN (%) No 434 ( 77.1) 50 ( 67.6) 27 ( 71.1) 0.008
Yes 15 ( 2.7) 8 ( 10.8) 1 ( 2.6)
No surg/Unk/NA 114 ( 20.2) 16 ( 21.6) 10 ( 26.3)
SURG_DISCHARGE_DAYS (mean (sd)) 1.05 (4.35) 1.00 (1.59) 0.35 (0.75) 0.695
READM_HOSP_30_DAYS_F (%) No_Surg_or_No_Readmit 529 ( 94.0) 68 ( 91.9) 30 ( 78.9) 0.001
Unplan_Readmit_Same 11 ( 2.0) 2 ( 2.7) 2 ( 5.3)
Plan_Readmit_Same 14 ( 2.5) 1 ( 1.4) 3 ( 7.9)
PlanUnplan_Same 0 ( 0.0) 0 ( 0.0) 1 ( 2.6)
9 9 ( 1.6) 3 ( 4.1) 2 ( 5.3)
RX_SUMM_RADIATION_F (%) None 409 ( 72.6) 34 ( 45.9) 22 ( 57.9) NaN
Beam Radiation 152 ( 27.0) 39 ( 52.7) 9 ( 23.7)
Radioactive Implants 1 ( 0.2) 1 ( 1.4) 0 ( 0.0)
Radioisotopes 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Beam + Imp or Isotopes 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Radiation, NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Unknown 1 ( 0.2) 0 ( 0.0) 7 ( 18.4)
PUF_30_DAY_MORT_CD_F (%) Alive_30 456 ( 81.0) 59 ( 79.7) 27 ( 71.1) 0.730
Dead_30 1 ( 0.2) 0 ( 0.0) 0 ( 0.0)
Unknown 4 ( 0.7) 1 ( 1.4) 1 ( 2.6)
NA 102 ( 18.1) 14 ( 18.9) 10 ( 26.3)
PUF_90_DAY_MORT_CD_F (%) Alive_90 450 ( 79.9) 59 ( 79.7) 27 ( 71.1) 0.857
Dead_90 3 ( 0.5) 0 ( 0.0) 0 ( 0.0)
Unknown 8 ( 1.4) 1 ( 1.4) 1 ( 2.6)
NA 102 ( 18.1) 14 ( 18.9) 10 ( 26.3)
DX_LASTCONTACT_DEATH_MONTHS (mean (sd)) 59.34 (42.73) 58.56 (42.82) 58.09 (44.79) 0.976
LYMPH_VASCULAR_INVASION_F (%) Neg_LymphVasc_Inv 139 ( 24.7) 17 ( 23.0) 6 ( 15.8) NaN
Pos_LumphVasc_Inv 2 ( 0.4) 8 ( 10.8) 0 ( 0.0)
N_A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Unknown 136 ( 24.2) 8 ( 10.8) 10 ( 26.3)
NA 286 ( 50.8) 41 ( 55.4) 22 ( 57.9)
RX_HOSP_SURG_APPR_2010_F (%) No_Surg 79 ( 14.0) 11 ( 14.9) 6 ( 15.8) NaN
Robot_Assist 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Robot_to_Open 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Endo_Lap 0 ( 0.0) 1 ( 1.4) 0 ( 0.0)
Endo_Lap_to_Open 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Open_Unknown 198 ( 35.2) 21 ( 28.4) 10 ( 26.3)
Unknown 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
NA 286 ( 50.8) 41 ( 55.4) 22 ( 57.9)
SURG_RAD_SEQ (%) Surg Alone 321 ( 57.0) 25 ( 33.8) 17 ( 44.7) NaN
Surg then Rad 141 ( 25.0) 33 ( 44.6) 7 ( 18.4)
Rad Alone 12 ( 2.1) 5 ( 6.8) 2 ( 5.3)
No Treatment 81 ( 14.4) 9 ( 12.2) 5 ( 13.2)
Other 8 ( 1.4) 0 ( 0.0) 7 ( 18.4)
Rad before and after Surg 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Rad then Surg 0 ( 0.0) 2 ( 2.7) 0 ( 0.0)
SURG_RAD_SEQ_C (%) Surg, No rad, No Chemo 321 ( 57.0) 0 ( 0.0) 0 ( 0.0) NaN
Surg then Rad, No Chemo 141 ( 25.0) 0 ( 0.0) 0 ( 0.0)
Surg then Rad, Yes Chemo 0 ( 0.0) 33 ( 44.6) 0 ( 0.0)
Surg, No rad, Yes Chemo 0 ( 0.0) 25 ( 33.8) 0 ( 0.0)
No Surg, No Rad, Yes Chemo 0 ( 0.0) 9 ( 12.2) 0 ( 0.0)
No Surg, No Rad, No Chemo 81 ( 14.4) 0 ( 0.0) 0 ( 0.0)
Other 8 ( 1.4) 0 ( 0.0) 38 (100.0)
Rad, No Surg, Yes Chemo 0 ( 0.0) 5 ( 6.8) 0 ( 0.0)
Rad, No Surg, No Chemo 12 ( 2.1) 0 ( 0.0) 0 ( 0.0)
Rad then Surg, Yes Chemo 0 ( 0.0) 2 ( 2.7) 0 ( 0.0)
Rad then Surg, No Chemo 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Rad before and after Surg, Yes Chemo 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Rad before and after Surg, No Chemo 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
T_SIZE (%) No Tumor 3 ( 0.5) 1 ( 1.4) 0 ( 0.0) <0.001
Microscopic focus 16 ( 2.8) 1 ( 1.4) 0 ( 0.0)
< 1 cm 74 ( 13.1) 4 ( 5.4) 6 ( 15.8)
1-2 cm 66 ( 11.7) 20 ( 27.0) 5 ( 13.2)
2-3 cm 41 ( 7.3) 11 ( 14.9) 1 ( 2.6)
3-4 cm 21 ( 3.7) 6 ( 8.1) 0 ( 0.0)
4-5 cm 5 ( 0.9) 5 ( 6.8) 1 ( 2.6)
5-6 cm 4 ( 0.7) 4 ( 5.4) 0 ( 0.0)
>6 cm 14 ( 2.5) 13 ( 17.6) 4 ( 10.5)
NA_unk 319 ( 56.7) 9 ( 12.2) 21 ( 55.3)
SURGERY_YN (%) No 93 ( 16.5) 14 ( 18.9) 8 ( 21.1) 0.175
Ukn 7 ( 1.2) 0 ( 0.0) 2 ( 5.3)
Yes 463 ( 82.2) 60 ( 81.1) 28 ( 73.7)
RADIATION_YN (%) No 409 ( 72.6) 34 ( 45.9) 23 ( 60.5) <0.001
Yes 153 ( 27.2) 40 ( 54.1) 9 ( 23.7)
NA 1 ( 0.2) 0 ( 0.0) 6 ( 15.8)
CHEMO_YN (%) No 563 (100.0) 0 ( 0.0) 0 ( 0.0) <0.001
Yes 0 ( 0.0) 74 (100.0) 0 ( 0.0)
Ukn 0 ( 0.0) 0 ( 0.0) 38 (100.0)
IMMUNO_YN (%) No 562 ( 99.8) 68 ( 91.9) 27 ( 71.1) <0.001
Yes 0 ( 0.0) 5 ( 6.8) 0 ( 0.0)
Ukn 1 ( 0.2) 1 ( 1.4) 11 ( 28.9)
Tx_YN (%) FALSE 80 ( 14.2) 0 ( 0.0) 0 ( 0.0) <0.001
TRUE 483 ( 85.8) 74 (100.0) 0 ( 0.0)
NA 0 ( 0.0) 0 ( 0.0) 38 (100.0)
mets_at_dx (%) Bone 4 ( 0.7) 6 ( 8.1) 0 ( 0.0) NaN
Brain 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Liver 0 ( 0.0) 1 ( 1.4) 0 ( 0.0)
Lung 3 ( 0.5) 0 ( 0.0) 1 ( 2.6)
None/Other/Unk/NA 556 ( 98.8) 67 ( 90.5) 37 ( 97.4)
MEDICAID_EXPN_CODE (%) Non-Expansion State 225 ( 40.0) 26 ( 35.1) 10 ( 26.3) <0.001
Jan 2014 Expansion States 174 ( 30.9) 24 ( 32.4) 9 ( 23.7)
Early Expansion States (2010-13) 61 ( 10.8) 7 ( 9.5) 9 ( 23.7)
Late Expansion States (> Jan 2014) 85 ( 15.1) 4 ( 5.4) 6 ( 15.8)
Suppressed for Ages 0 - 39 18 ( 3.2) 13 ( 17.6) 4 ( 10.5)

p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN", 
                 "CHEMO_YN", "mets_at_dx", "IMMUNO_YN", "Tx_YN",
                 "MEDICAID_EXPN_CODE"), 
        strata = "Tx_YN")
no non-missing arguments to min; returning Infno non-missing arguments to min; returning Infno non-missing arguments to min; returning Infno non-missing arguments to max; returning -Infno non-missing arguments to max; returning -Infno non-missing arguments to max; returning -InfVariable has only NA's in at least one stratum. na.rm turned off.Variable has only NA's in at least one stratum. na.rm turned off.Variable has only NA's in at least one stratum. na.rm turned off.
level FALSE TRUE p test
n 80 557
FACILITY_TYPE_F (%) Community Cancer Program 14 ( 17.5) 64 ( 11.5) 0.294
Comprehensive Comm Ca Program 36 ( 45.0) 240 ( 43.1)
Academic/Research Program 20 ( 25.0) 147 ( 26.4)
Integrated Network Ca Program 9 ( 11.2) 76 ( 13.6)
NA 1 ( 1.2) 30 ( 5.4)
FACILITY_LOCATION_F (%) New England 8 ( 10.0) 22 ( 3.9) 0.012
Middle Atlantic 18 ( 22.5) 66 ( 11.8)
South Atlantic 15 ( 18.8) 122 ( 21.9)
East North Central 16 ( 20.0) 113 ( 20.3)
East South Central 5 ( 6.2) 38 ( 6.8)
West North Central 7 ( 8.8) 49 ( 8.8)
West South Central 8 ( 10.0) 47 ( 8.4)
Mountain 0 ( 0.0) 33 ( 5.9)
Pacific 2 ( 2.5) 37 ( 6.6)
NA 1 ( 1.2) 30 ( 5.4)
FACILITY_GEOGRAPHY (%) Northeast 26 ( 32.5) 88 ( 15.8) 0.001
South 23 ( 28.7) 169 ( 30.3)
Midwest 28 ( 35.0) 200 ( 35.9)
West 2 ( 2.5) 70 ( 12.6)
NA 1 ( 1.2) 30 ( 5.4)
AGE (mean (sd)) 74.69 (14.06) 64.35 (14.71) <0.001
AGE_F (%) (0,54] 7 ( 8.8) 142 ( 25.5) <0.001
(54,64] 17 ( 21.2) 128 ( 23.0)
(64,74] 9 ( 11.2) 130 ( 23.3)
(74,100] 47 ( 58.8) 157 ( 28.2)
AGE_40 (%) (0,40] 1 ( 1.2) 32 ( 5.7) 0.154
(40,100] 79 ( 98.8) 525 ( 94.3)
SEX_F (%) Male 2 ( 2.5) 16 ( 2.9) 1.000
Female 78 ( 97.5) 541 ( 97.1)
RACE_F (%) White 63 ( 78.8) 479 ( 86.0) 0.249
Black 14 ( 17.5) 56 ( 10.1)
Other/Unk 2 ( 2.5) 12 ( 2.2)
Asian 1 ( 1.2) 10 ( 1.8)
HISPANIC (%) No 72 ( 90.0) 484 ( 86.9) 0.685
Yes 3 ( 3.8) 22 ( 3.9)
Unknown 5 ( 6.2) 51 ( 9.2)
INSURANCE_F (%) Private 17 ( 21.2) 242 ( 43.4) 0.001
None 5 ( 6.2) 17 ( 3.1)
Medicaid 5 ( 6.2) 28 ( 5.0)
Medicare 48 ( 60.0) 253 ( 45.4)
Other Government 0 ( 0.0) 8 ( 1.4)
Unknown 5 ( 6.2) 9 ( 1.6)
INCOME_F (%) Less than $38,000 18 ( 22.5) 104 ( 18.7) 0.888
$38,000 - $47,999 16 ( 20.0) 119 ( 21.4)
$48,000 - $62,999 22 ( 27.5) 150 ( 26.9)
$63,000 + 23 ( 28.7) 180 ( 32.3)
NA 1 ( 1.2) 4 ( 0.7)
EDUCATION_F (%) 21% or more 17 ( 21.2) 73 ( 13.1) 0.257
13 - 20.9% 20 ( 25.0) 134 ( 24.1)
7 - 12.9% 25 ( 31.2) 189 ( 33.9)
Less than 7% 17 ( 21.2) 158 ( 28.4)
NA 1 ( 1.2) 3 ( 0.5)
U_R_F (%) Metro 68 ( 85.0) 456 ( 81.9) 0.901
Urban 9 ( 11.2) 76 ( 13.6)
Rural 1 ( 1.2) 11 ( 2.0)
NA 2 ( 2.5) 14 ( 2.5)
CROWFLY (mean (sd)) 15.47 (20.54) 25.16 (94.89) 0.366
CDCC_TOTAL_BEST (%) 0 62 ( 77.5) 465 ( 83.5) 0.130
1 9 ( 11.2) 65 ( 11.7)
2 5 ( 6.2) 17 ( 3.1)
3 4 ( 5.0) 10 ( 1.8)
SITE_TEXT (%) C00.0 External Lip: Upper NOS 0 ( 0.0) 0 ( 0.0) NaN
C00.1 External Lip: Lower NOS 0 ( 0.0) 0 ( 0.0)
C00.2 External Lip: NOS 0 ( 0.0) 0 ( 0.0)
C00.3 Lip: Upper Mucosa 0 ( 0.0) 0 ( 0.0)
C00.4 Lip: Lower Mucosa 0 ( 0.0) 0 ( 0.0)
C00.5 Lip: Mucosa NOS 0 ( 0.0) 0 ( 0.0)
C00.6 Lip: Commissure 0 ( 0.0) 0 ( 0.0)
C00.8 Lip: Overlapping 0 ( 0.0) 0 ( 0.0)
C00.9 Lip NOS 0 ( 0.0) 0 ( 0.0)
C01.9 Tongue: Base NOS 0 ( 0.0) 0 ( 0.0)
C02.0 Tongue: Dorsal NOS 0 ( 0.0) 0 ( 0.0)
C02.1 Tongue: Border, Tip 0 ( 0.0) 0 ( 0.0)
C02.2 Tongue: Ventral NOS 0 ( 0.0) 0 ( 0.0)
C02.3 Tongue: Anterior NOS 0 ( 0.0) 0 ( 0.0)
C02.4 Lingual Tonsil 0 ( 0.0) 0 ( 0.0)
C02.8 Tongue: Overlapping 0 ( 0.0) 0 ( 0.0)
C02.9 Tongue: NOS 0 ( 0.0) 0 ( 0.0)
C03.0 Gum: Upper 0 ( 0.0) 0 ( 0.0)
C03.1 Gum: Lower 0 ( 0.0) 0 ( 0.0)
C03.9 Gum NOS 0 ( 0.0) 0 ( 0.0)
C04.0 Mouth: Anterior Floor 0 ( 0.0) 0 ( 0.0)
C04.1 Mouth: Lateral Floor 0 ( 0.0) 0 ( 0.0)
C04.9 Floor of Mouth NOS 0 ( 0.0) 0 ( 0.0)
C05.0 Hard Palate 0 ( 0.0) 0 ( 0.0)
C05.1 Soft Palate NOS 0 ( 0.0) 0 ( 0.0)
C05.2 Uvula 0 ( 0.0) 0 ( 0.0)
C05.8 Palate: Overlapping 0 ( 0.0) 0 ( 0.0)
C05.9 Palate NOS 0 ( 0.0) 0 ( 0.0)
C06.0 Cheek Mucosa 0 ( 0.0) 0 ( 0.0)
C06.1 Mouth: Vestibule 0 ( 0.0) 0 ( 0.0)
C06.2 Retromolar Area 0 ( 0.0) 0 ( 0.0)
C06.8 Mouth: Other Overlapping 0 ( 0.0) 0 ( 0.0)
C06.9 Mouth NOS 0 ( 0.0) 0 ( 0.0)
C07.9 Parotid Gland 0 ( 0.0) 0 ( 0.0)
C09.8 Tonsil: Overlapping 0 ( 0.0) 0 ( 0.0)
C09.9 Tonsil NOS 0 ( 0.0) 0 ( 0.0)
C11.1 Nasopharynx: Poster Wall 0 ( 0.0) 0 ( 0.0)
C14.2 Waldeyer Ring 0 ( 0.0) 0 ( 0.0)
C30.0 Nasal Cavity 0 ( 0.0) 0 ( 0.0)
C37.9 Thymus 0 ( 0.0) 0 ( 0.0)
C42.0 Blood 0 ( 0.0) 0 ( 0.0)
C42.2 Spleen 0 ( 0.0) 0 ( 0.0)
C42.4 Hematopoietic NOS 0 ( 0.0) 0 ( 0.0)
C44.0 Skin of lip, NOS 0 ( 0.0) 0 ( 0.0)
C44.1 Eyelid 0 ( 0.0) 0 ( 0.0)
C44.2 External ear 0 ( 0.0) 0 ( 0.0)
C44.3 Skin of ear and unspecified parts of face 0 ( 0.0) 0 ( 0.0)
C44.4 Skin of scalp and neck 0 ( 0.0) 0 ( 0.0)
C44.5 Skin of trunk 0 ( 0.0) 0 ( 0.0)
C44.6 Skin of upper limb and shoulder 0 ( 0.0) 0 ( 0.0)
C44.7 Skin of lower limb and hip 0 ( 0.0) 0 ( 0.0)
C44.8 Overlapping lesion of skin 0 ( 0.0) 0 ( 0.0)
C44.9 Skin, NOS 0 ( 0.0) 0 ( 0.0)
C50.0 Nipple 41 ( 51.2) 382 ( 68.6)
C51.0 Labium majus 0 ( 0.0) 0 ( 0.0)
C51.1 Labium minus 0 ( 0.0) 0 ( 0.0)
C51.2 Clitoris 0 ( 0.0) 0 ( 0.0)
C51.8 Overlapping lesion of vulva 0 ( 0.0) 0 ( 0.0)
C51.9 Vulva, NOS 0 ( 0.0) 0 ( 0.0)
C52.9 Vagina, NOS 0 ( 0.0) 0 ( 0.0)
C60.0 Prepuce 0 ( 0.0) 0 ( 0.0)
C60.1 Glans penis 0 ( 0.0) 0 ( 0.0)
C60.2 Body of penis 0 ( 0.0) 0 ( 0.0)
C60.8 Overlapping lesion of penis 0 ( 0.0) 0 ( 0.0)
C60.9 Penis 0 ( 0.0) 0 ( 0.0)
C63.2 Scrotum, NOS 0 ( 0.0) 0 ( 0.0)
C77.0 Lymph Nodes: HeadFaceNeck 0 ( 0.0) 0 ( 0.0)
C77.1 Intrathoracic Lymph Nodes 0 ( 0.0) 0 ( 0.0)
C77.2 Intra-abdominal LymphNodes 0 ( 0.0) 0 ( 0.0)
C77.3 Lymph Nodes of axilla or arm 0 ( 0.0) 0 ( 0.0)
C77.4 Lymph Nodes: Leg 0 ( 0.0) 0 ( 0.0)
C77.5 Pelvic Lymph Nodes 0 ( 0.0) 0 ( 0.0)
C77.8 Lymph Nodes: multiple region 0 ( 0.0) 0 ( 0.0)
C77.9 Lymph Node NOS 0 ( 0.0) 0 ( 0.0)
NA 39 ( 48.8) 175 ( 31.4)
BEHAVIOR (%) 2 0 ( 0.0) 0 ( 0.0) NaN
3 80 (100.0) 557 (100.0)
GRADE_F (%) Gr I: Well Diff 6 ( 7.5) 15 ( 2.7) NaN
Gr II: Mod Diff 4 ( 5.0) 50 ( 9.0)
Gr III: Poor Diff 5 ( 6.2) 91 ( 16.3)
Gr IV: Undiff/Anaplastic 0 ( 0.0) 2 ( 0.4)
5 0 ( 0.0) 0 ( 0.0)
6 0 ( 0.0) 0 ( 0.0)
7 0 ( 0.0) 0 ( 0.0)
8 0 ( 0.0) 0 ( 0.0)
NA/Unkown 65 ( 81.2) 399 ( 71.6)
DX_STAGING_PROC_DAYS (mean (sd)) 1.97 (7.63) 1.73 (10.87) 0.865
TNM_CLIN_T (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
c0 0 ( 0.0) 5 ( 0.9)
c1 4 ( 5.0) 30 ( 5.4)
c1A 1 ( 1.2) 11 ( 2.0)
c1B 4 ( 5.0) 4 ( 0.7)
c1C 3 ( 3.8) 12 ( 2.2)
c1MI 0 ( 0.0) 4 ( 0.7)
c2 5 ( 6.2) 16 ( 2.9)
c2A 0 ( 0.0) 0 ( 0.0)
c2B 0 ( 0.0) 0 ( 0.0)
c2C 0 ( 0.0) 0 ( 0.0)
c2D 0 ( 0.0) 0 ( 0.0)
c3 3 ( 3.8) 15 ( 2.7)
c3A 0 ( 0.0) 0 ( 0.0)
c3B 0 ( 0.0) 0 ( 0.0)
c4 4 ( 5.0) 6 ( 1.1)
c4A 0 ( 0.0) 0 ( 0.0)
c4B 2 ( 2.5) 7 ( 1.3)
c4C 0 ( 0.0) 0 ( 0.0)
c4D 1 ( 1.2) 2 ( 0.4)
cX 23 ( 28.7) 151 ( 27.1)
pA 0 ( 0.0) 0 ( 0.0)
pIS 23 ( 28.7) 266 ( 47.8)
NA 7 ( 8.8) 28 ( 5.0)
TNM_CLIN_N (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
c0 44 ( 55.0) 385 ( 69.1)
c1 7 ( 8.8) 19 ( 3.4)
c1A 0 ( 0.0) 0 ( 0.0)
c1B 0 ( 0.0) 0 ( 0.0)
c2 0 ( 0.0) 4 ( 0.7)
c2A 0 ( 0.0) 2 ( 0.4)
c2B 1 ( 1.2) 0 ( 0.0)
c2C 0 ( 0.0) 0 ( 0.0)
c3 1 ( 1.2) 2 ( 0.4)
c3A 0 ( 0.0) 1 ( 0.2)
c3B 0 ( 0.0) 0 ( 0.0)
c3C 0 ( 0.0) 0 ( 0.0)
c4 0 ( 0.0) 0 ( 0.0)
cX 20 ( 25.0) 126 ( 22.6)
NA 7 ( 8.8) 18 ( 3.2)
TNM_CLIN_M (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
c0 61 ( 76.2) 520 ( 93.4)
c0I+ 0 ( 0.0) 0 ( 0.0)
c1 8 ( 10.0) 16 ( 2.9)
c1A 0 ( 0.0) 0 ( 0.0)
c1B 0 ( 0.0) 0 ( 0.0)
c1C 0 ( 0.0) 0 ( 0.0)
NA 11 ( 13.8) 21 ( 3.8)
TNM_CLIN_STAGE_GROUP (%) 0 27 ( 33.8) 288 ( 51.7) NaN
1 6 ( 7.5) 28 ( 5.0)
1A 4 ( 5.0) 28 ( 5.0)
1B 0 ( 0.0) 1 ( 0.2)
1C 0 ( 0.0) 0 ( 0.0)
2 0 ( 0.0) 0 ( 0.0)
2A 2 ( 2.5) 21 ( 3.8)
2B 4 ( 5.0) 9 ( 1.6)
2C 0 ( 0.0) 0 ( 0.0)
3 1 ( 1.2) 1 ( 0.2)
3A 1 ( 1.2) 6 ( 1.1)
3B 3 ( 3.8) 9 ( 1.6)
3C 1 ( 1.2) 2 ( 0.4)
4 9 ( 11.2) 16 ( 2.9)
4A 0 ( 0.0) 0 ( 0.0)
4A1 0 ( 0.0) 0 ( 0.0)
4A2 0 ( 0.0) 0 ( 0.0)
4B 0 ( 0.0) 0 ( 0.0)
4C 0 ( 0.0) 0 ( 0.0)
N_A 0 ( 0.0) 0 ( 0.0)
99 22 ( 27.5) 148 ( 26.6)
TNM_PATH_T (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
p0 0 ( 0.0) 8 ( 1.4)
p1 0 ( 0.0) 12 ( 2.2)
p1A 0 ( 0.0) 17 ( 3.1)
p1B 0 ( 0.0) 10 ( 1.8)
p1C 0 ( 0.0) 23 ( 4.1)
p1MI 0 ( 0.0) 7 ( 1.3)
p2 0 ( 0.0) 12 ( 2.2)
p2A 0 ( 0.0) 0 ( 0.0)
p2B 0 ( 0.0) 0 ( 0.0)
p2C 0 ( 0.0) 0 ( 0.0)
p2D 0 ( 0.0) 0 ( 0.0)
p3 0 ( 0.0) 2 ( 0.4)
p3A 0 ( 0.0) 0 ( 0.0)
p3B 0 ( 0.0) 0 ( 0.0)
p4 1 ( 1.2) 0 ( 0.0)
p4A 0 ( 0.0) 0 ( 0.0)
p4B 0 ( 0.0) 5 ( 0.9)
p4C 0 ( 0.0) 0 ( 0.0)
p4D 1 ( 1.2) 2 ( 0.4)
pA 0 ( 0.0) 0 ( 0.0)
pIS 5 ( 6.2) 238 ( 42.7)
pX 44 ( 55.0) 196 ( 35.2)
NA 29 ( 36.2) 25 ( 4.5)
TNM_PATH_N (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
p0 2 ( 2.5) 216 ( 38.8)
p0I- 0 ( 0.0) 29 ( 5.2)
p0I+ 0 ( 0.0) 2 ( 0.4)
p0M- 0 ( 0.0) 0 ( 0.0)
p0M+ 0 ( 0.0) 0 ( 0.0)
p1 0 ( 0.0) 7 ( 1.3)
p1A 1 ( 1.2) 8 ( 1.4)
p1B 0 ( 0.0) 0 ( 0.0)
p1C 0 ( 0.0) 0 ( 0.0)
p1MI 0 ( 0.0) 2 ( 0.4)
p2 0 ( 0.0) 2 ( 0.4)
p2A 0 ( 0.0) 2 ( 0.4)
p2B 0 ( 0.0) 0 ( 0.0)
p2C 0 ( 0.0) 0 ( 0.0)
p3 0 ( 0.0) 2 ( 0.4)
p3A 0 ( 0.0) 3 ( 0.5)
p3B 0 ( 0.0) 0 ( 0.0)
p3C 0 ( 0.0) 0 ( 0.0)
p4 0 ( 0.0) 0 ( 0.0)
pX 47 ( 58.8) 239 ( 42.9)
NA 30 ( 37.5) 45 ( 8.1)
TNM_PATH_M (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
p0 0 ( 0.0) 0 ( 0.0)
p1 2 ( 2.5) 6 ( 1.1)
p1A 0 ( 0.0) 0 ( 0.0)
p1B 0 ( 0.0) 0 ( 0.0)
p1C 0 ( 0.0) 0 ( 0.0)
pX 32 ( 40.0) 290 ( 52.1)
NA 46 ( 57.5) 261 ( 46.9)
TNM_PATH_STAGE_GROUP (%) 0 2 ( 2.5) 279 ( 50.1) NaN
1 0 ( 0.0) 51 ( 9.2)
1A 0 ( 0.0) 26 ( 4.7)
1B 0 ( 0.0) 2 ( 0.4)
1C 0 ( 0.0) 0 ( 0.0)
2 0 ( 0.0) 3 ( 0.5)
2A 0 ( 0.0) 21 ( 3.8)
2B 0 ( 0.0) 8 ( 1.4)
2C 0 ( 0.0) 0 ( 0.0)
3 0 ( 0.0) 0 ( 0.0)
3A 0 ( 0.0) 4 ( 0.7)
3B 0 ( 0.0) 6 ( 1.1)
3C 1 ( 1.2) 4 ( 0.7)
4 2 ( 2.5) 7 ( 1.3)
4A 0 ( 0.0) 0 ( 0.0)
4A1 0 ( 0.0) 0 ( 0.0)
4B 0 ( 0.0) 0 ( 0.0)
4C 0 ( 0.0) 0 ( 0.0)
N_A 0 ( 0.0) 0 ( 0.0)
99 55 ( 68.8) 129 ( 23.2)
NA 20 ( 25.0) 17 ( 3.1)
DX_RX_STARTED_DAYS (mean (sd)) 67.82 (96.52) 34.85 (30.65) <0.001
DX_SURG_STARTED_DAYS (mean (sd)) NaN (NA) 39.70 (40.59) NA
DX_DEFSURG_STARTED_DAYS (mean (sd)) NaN (NA) 47.18 (46.91) NA
MARGINS (%) No Residual 0 ( 0.0) 484 ( 86.9) NaN
Residual, NOS 0 ( 0.0) 10 ( 1.8)
Microscopic Resid 0 ( 0.0) 11 ( 2.0)
Macroscopic Resid 0 ( 0.0) 2 ( 0.4)
Not evaluable 0 ( 0.0) 0 ( 0.0)
No surg 80 (100.0) 33 ( 5.9)
Unknown 0 ( 0.0) 17 ( 3.1)
MARGINS_YN (%) No 0 ( 0.0) 484 ( 86.9) <0.001
Yes 0 ( 0.0) 23 ( 4.1)
No surg/Unk/NA 80 (100.0) 50 ( 9.0)
SURG_DISCHARGE_DAYS (mean (sd)) NaN (NA) 1.04 (4.16) NA
READM_HOSP_30_DAYS_F (%) No_Surg_or_No_Readmit 77 ( 96.2) 520 ( 93.4) NaN
Unplan_Readmit_Same 0 ( 0.0) 13 ( 2.3)
Plan_Readmit_Same 0 ( 0.0) 15 ( 2.7)
PlanUnplan_Same 0 ( 0.0) 0 ( 0.0)
9 3 ( 3.8) 9 ( 1.6)
RX_SUMM_RADIATION_F (%) None 80 (100.0) 363 ( 65.2) NaN
Beam Radiation 0 ( 0.0) 191 ( 34.3)
Radioactive Implants 0 ( 0.0) 2 ( 0.4)
Radioisotopes 0 ( 0.0) 0 ( 0.0)
Beam + Imp or Isotopes 0 ( 0.0) 0 ( 0.0)
Radiation, NOS 0 ( 0.0) 0 ( 0.0)
Unknown 0 ( 0.0) 1 ( 0.2)
PUF_30_DAY_MORT_CD_F (%) Alive_30 0 ( 0.0) 515 ( 92.5) <0.001
Dead_30 0 ( 0.0) 1 ( 0.2)
Unknown 0 ( 0.0) 5 ( 0.9)
NA 80 (100.0) 36 ( 6.5)
PUF_90_DAY_MORT_CD_F (%) Alive_90 0 ( 0.0) 509 ( 91.4) <0.001
Dead_90 0 ( 0.0) 3 ( 0.5)
Unknown 0 ( 0.0) 9 ( 1.6)
NA 80 (100.0) 36 ( 6.5)
DX_LASTCONTACT_DEATH_MONTHS (mean (sd)) 28.26 (29.96) 63.70 (42.43) <0.001
LYMPH_VASCULAR_INVASION_F (%) Neg_LymphVasc_Inv 9 ( 11.2) 147 ( 26.4) NaN
Pos_LumphVasc_Inv 0 ( 0.0) 10 ( 1.8)
N_A 0 ( 0.0) 0 ( 0.0)
Unknown 38 ( 47.5) 106 ( 19.0)
NA 33 ( 41.2) 294 ( 52.8)
RX_HOSP_SURG_APPR_2010_F (%) No_Surg 47 ( 58.8) 43 ( 7.7) NaN
Robot_Assist 0 ( 0.0) 0 ( 0.0)
Robot_to_Open 0 ( 0.0) 0 ( 0.0)
Endo_Lap 0 ( 0.0) 1 ( 0.2)
Endo_Lap_to_Open 0 ( 0.0) 0 ( 0.0)
Open_Unknown 0 ( 0.0) 219 ( 39.3)
Unknown 0 ( 0.0) 0 ( 0.0)
NA 33 ( 41.2) 294 ( 52.8)
SURG_RAD_SEQ (%) Surg Alone 0 ( 0.0) 346 ( 62.1) NaN
Surg then Rad 0 ( 0.0) 174 ( 31.2)
Rad Alone 0 ( 0.0) 17 ( 3.1)
No Treatment 80 (100.0) 10 ( 1.8)
Other 0 ( 0.0) 8 ( 1.4)
Rad before and after Surg 0 ( 0.0) 0 ( 0.0)
Rad then Surg 0 ( 0.0) 2 ( 0.4)
SURG_RAD_SEQ_C (%) Surg, No rad, No Chemo 0 ( 0.0) 321 ( 57.6) NaN
Surg then Rad, No Chemo 0 ( 0.0) 141 ( 25.3)
Surg then Rad, Yes Chemo 0 ( 0.0) 33 ( 5.9)
Surg, No rad, Yes Chemo 0 ( 0.0) 25 ( 4.5)
No Surg, No Rad, Yes Chemo 0 ( 0.0) 9 ( 1.6)
No Surg, No Rad, No Chemo 80 (100.0) 1 ( 0.2)
Other 0 ( 0.0) 8 ( 1.4)
Rad, No Surg, Yes Chemo 0 ( 0.0) 5 ( 0.9)
Rad, No Surg, No Chemo 0 ( 0.0) 12 ( 2.2)
Rad then Surg, Yes Chemo 0 ( 0.0) 2 ( 0.4)
Rad then Surg, No Chemo 0 ( 0.0) 0 ( 0.0)
Rad before and after Surg, Yes Chemo 0 ( 0.0) 0 ( 0.0)
Rad before and after Surg, No Chemo 0 ( 0.0) 0 ( 0.0)
T_SIZE (%) No Tumor 2 ( 2.5) 2 ( 0.4) 0.050
Microscopic focus 0 ( 0.0) 17 ( 3.1)
< 1 cm 10 ( 12.5) 68 ( 12.2)
1-2 cm 4 ( 5.0) 82 ( 14.7)
2-3 cm 5 ( 6.2) 47 ( 8.4)
3-4 cm 5 ( 6.2) 22 ( 3.9)
4-5 cm 2 ( 2.5) 8 ( 1.4)
5-6 cm 1 ( 1.2) 7 ( 1.3)
>6 cm 6 ( 7.5) 21 ( 3.8)
NA_unk 45 ( 56.2) 283 ( 50.8)
SURGERY_YN (%) No 80 (100.0) 27 ( 4.8) <0.001
Ukn 0 ( 0.0) 7 ( 1.3)
Yes 0 ( 0.0) 523 ( 93.9)
RADIATION_YN (%) No 80 (100.0) 363 ( 65.2) <0.001
Yes 0 ( 0.0) 193 ( 34.6)
NA 0 ( 0.0) 1 ( 0.2)
CHEMO_YN (%) No 80 (100.0) 483 ( 86.7) NaN
Yes 0 ( 0.0) 74 ( 13.3)
Ukn 0 ( 0.0) 0 ( 0.0)
mets_at_dx (%) Bone 3 ( 3.8) 7 ( 1.3) NaN
Brain 0 ( 0.0) 0 ( 0.0)
Liver 0 ( 0.0) 1 ( 0.2)
Lung 1 ( 1.2) 2 ( 0.4)
None/Other/Unk/NA 76 ( 95.0) 547 ( 98.2)
IMMUNO_YN (%) No 80 (100.0) 550 ( 98.7) 0.602
Yes 0 ( 0.0) 5 ( 0.9)
Ukn 0 ( 0.0) 2 ( 0.4)
Tx_YN (%) FALSE 80 (100.0) 0 ( 0.0) NaN
TRUE 0 ( 0.0) 557 (100.0)
NA 0 ( 0.0) 0 ( 0.0)
MEDICAID_EXPN_CODE (%) Non-Expansion State 31 ( 38.8) 220 ( 39.5) 0.566
Jan 2014 Expansion States 28 ( 35.0) 170 ( 30.5)
Early Expansion States (2010-13) 9 ( 11.2) 59 ( 10.6)
Late Expansion States (> Jan 2014) 11 ( 13.8) 78 ( 14.0)
Suppressed for Ages 0 - 39 1 ( 1.2) 30 ( 5.4)

Kaplan Meier Analysis

All

uni_var(test_var = "All", data_imp = data)
_________________________________________________
   
## All
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ All, data = data)

      n  events  median 0.95LCL 0.95UCL 
    675     180     138     133      NA 

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ All, data = data)

 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    564      46    0.929  0.0102        0.909        0.949
   24    497      23    0.889  0.0127        0.864        0.914
   36    427      25    0.842  0.0151        0.813        0.872
   48    359      19    0.802  0.0169        0.770        0.836
   60    310      13    0.772  0.0183        0.737        0.808
  120     79      46    0.596  0.0282        0.543        0.653



   
## Univariable Cox Proportional Hazard Model for:  All

[1] "Only one level, no Cox model performed"




   
## Unadjusted Kaplan Meier Overall Survival Curve for:  All

Facility Type

uni_var(test_var = "FACILITY_TYPE_F", data_imp = data)
_________________________________________________
   
## FACILITY_TYPE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_TYPE_F, data = data)

   35 observations deleted due to missingness 
                                                n events median 0.95LCL 0.95UCL
FACILITY_TYPE_F=Community Cancer Program       81     26    138     108      NA
FACILITY_TYPE_F=Comprehensive Comm Ca Program 296     83    134     108      NA
FACILITY_TYPE_F=Academic/Research Program     173     44     NA     137      NA
FACILITY_TYPE_F=Integrated Network Ca Program  90     26    133     128      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_TYPE_F, data = data)

35 observations deleted due to missingness 
                FACILITY_TYPE_F=Community Cancer Program 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     66       7    0.909  0.0328        0.847        0.976
   24     60       4    0.853  0.0411        0.776        0.937
   36     51       2    0.822  0.0449        0.739        0.915
   48     45       6    0.726  0.0543        0.627        0.840
   60     39       1    0.708  0.0558        0.607        0.826
  120     11       5    0.573  0.0722        0.448        0.734

                FACILITY_TYPE_F=Comprehensive Comm Ca Program 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    244      23    0.918  0.0164        0.886        0.951
   24    210       9    0.882  0.0196        0.844        0.921
   36    172      13    0.824  0.0240        0.779        0.873
   48    141       6    0.794  0.0262        0.744        0.847
   60    121       6    0.758  0.0289        0.703        0.816
  120     30      23    0.540  0.0469        0.456        0.640

                FACILITY_TYPE_F=Academic/Research Program 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    146       9    0.946  0.0175        0.912        0.981
   24    128       7    0.899  0.0241        0.853        0.947
   36    118       5    0.862  0.0281        0.809        0.919
   48     99       5    0.824  0.0317        0.764        0.888
   60     87       3    0.798  0.0340        0.734        0.868
  120     24      14    0.605  0.0533        0.509        0.720

                FACILITY_TYPE_F=Integrated Network Ca Program 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     76       7    0.919  0.0294        0.863        0.978
   24     70       3    0.882  0.0352        0.815        0.953
   36     59       4    0.828  0.0420        0.750        0.915
   48     51       2    0.799  0.0454        0.714        0.893
   60     43       3    0.749  0.0509        0.656        0.856
  120     10       4    0.648  0.0653        0.532        0.790




   
## Univariable Cox Proportional Hazard Model for:  FACILITY_TYPE_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_TYPE_F, data = data)

  n= 640, number of events= 179 
   (35 observations deleted due to missingness)

                                                  coef exp(coef)  se(coef)      z Pr(>|z|)
FACILITY_TYPE_FComprehensive Comm Ca Program  0.001391  1.001392  0.225451  0.006    0.995
FACILITY_TYPE_FAcademic/Research Program     -0.223626  0.799614  0.247849 -0.902    0.367
FACILITY_TYPE_FIntegrated Network Ca Program -0.085972  0.917620  0.277593 -0.310    0.757

                                             exp(coef) exp(-coef) lower .95 upper .95
FACILITY_TYPE_FComprehensive Comm Ca Program    1.0014     0.9986    0.6437     1.558
FACILITY_TYPE_FAcademic/Research Program        0.7996     1.2506    0.4919     1.300
FACILITY_TYPE_FIntegrated Network Ca Program    0.9176     1.0898    0.5326     1.581

Concordance= 0.525  (se = 0.022 )
Rsquare= 0.003   (max possible= 0.961 )
Likelihood ratio test= 1.63  on 3 df,   p=0.6515
Wald test            = 1.59  on 3 df,   p=0.6614
Score (logrank) test = 1.6  on 3 df,   p=0.66
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  FACILITY_TYPE_F

Facility Location

uni_var(test_var = "FACILITY_LOCATION_F", data_imp = data)
_________________________________________________
   
## FACILITY_LOCATION_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_LOCATION_F, data = data)

   35 observations deleted due to missingness 
                                         n events median 0.95LCL 0.95UCL
FACILITY_LOCATION_F=New England         31      7     NA    88.7      NA
FACILITY_LOCATION_F=Middle Atlantic     93     23    138   108.0      NA
FACILITY_LOCATION_F=South Atlantic     143     36    137   133.0      NA
FACILITY_LOCATION_F=East North Central 134     37    137   127.7      NA
FACILITY_LOCATION_F=East South Central  44     18    110    73.2      NA
FACILITY_LOCATION_F=West North Central  59     22    102    83.6      NA
FACILITY_LOCATION_F=West South Central  60     21    115    67.6      NA
FACILITY_LOCATION_F=Mountain            35      6     NA      NA      NA
FACILITY_LOCATION_F=Pacific             41      9     NA    94.5      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_LOCATION_F, data = data)

35 observations deleted due to missingness 
                FACILITY_LOCATION_F=New England 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     23       3    0.895  0.0575        0.790         1.00
   24     21       0    0.895  0.0575        0.790         1.00
   36     16       1    0.843  0.0744        0.709         1.00
   48     13       2    0.737  0.0954        0.572         0.95
   60     12       0    0.737  0.0954        0.572         0.95
  120      5       1    0.664  0.1107        0.479         0.92

                FACILITY_LOCATION_F=Middle Atlantic 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     77       6    0.933  0.0266        0.882        0.986
   24     67       3    0.895  0.0331        0.833        0.963
   36     62       0    0.895  0.0331        0.833        0.963
   48     51       5    0.820  0.0442        0.738        0.912
   60     46       1    0.804  0.0464        0.718        0.900
  120      9       7    0.597  0.0794        0.460        0.775

                FACILITY_LOCATION_F=South Atlantic 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    123       5    0.963  0.0160        0.933        0.995
   24    104       7    0.906  0.0260        0.856        0.958
   36     87       4    0.869  0.0308        0.810        0.931
   48     75       4    0.827  0.0358        0.759        0.900
   60     64       4    0.781  0.0406        0.705        0.864
  120     19       8    0.624  0.0609        0.515        0.755

                FACILITY_LOCATION_F=East North Central 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    114       8    0.938  0.0212        0.897        0.981
   24    103       5    0.896  0.0274        0.844        0.951
   36     93       5    0.851  0.0325        0.790        0.917
   48     76       6    0.792  0.0382        0.721        0.871
   60     64       3    0.760  0.0410        0.683        0.844
  120     17       7    0.646  0.0538        0.549        0.761

                FACILITY_LOCATION_F=East South Central 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     35       7    0.834  0.0572        0.729        0.954
   24     34       1    0.810  0.0604        0.700        0.938
   36     30       4    0.715  0.0696        0.591        0.865
   48     29       0    0.715  0.0696        0.591        0.865
   60     25       0    0.715  0.0696        0.591        0.865
  120      2       6    0.452  0.1102        0.280        0.729

                FACILITY_LOCATION_F=West North Central 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     52       4    0.930  0.0339        0.866        0.999
   24     44       5    0.838  0.0496        0.746        0.941
   36     39       3    0.779  0.0566        0.675        0.898
   48     33       2    0.739  0.0604        0.629        0.867
   60     28       2    0.690  0.0654        0.573        0.831
  120      5       6    0.430  0.0973        0.276        0.670

                FACILITY_LOCATION_F=West South Central 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     46       7    0.876  0.0439        0.794        0.966
   24     39       1    0.857  0.0469        0.770        0.954
   36     32       3    0.791  0.0567        0.687        0.910
   48     28       0    0.791  0.0567        0.687        0.910
   60     24       2    0.731  0.0665        0.612        0.874
  120      5       8    0.396  0.1016        0.239        0.655

                FACILITY_LOCATION_F=Mountain 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     28       3    0.911  0.0492        0.819        1.000
   24     26       0    0.911  0.0492        0.819        1.000
   36     22       2    0.838  0.0670        0.716        0.980
   48     16       0    0.838  0.0670        0.716        0.980
   60     15       0    0.838  0.0670        0.716        0.980
  120      8       1    0.768  0.0908        0.609        0.968

                FACILITY_LOCATION_F=Pacific 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     34       3    0.923  0.0429        0.842        1.000
   24     30       1    0.893  0.0508        0.799        0.998
   36     19       2    0.823  0.0670        0.701        0.965
   48     15       0    0.823  0.0670        0.701        0.965
   60     12       1    0.759  0.0867        0.607        0.950
  120      5       2    0.621  0.1133        0.435        0.888




   
## Univariable Cox Proportional Hazard Model for:  FACILITY_LOCATION_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_LOCATION_F, data = data)

  n= 640, number of events= 179 
   (35 observations deleted due to missingness)

                                          coef exp(coef) se(coef)      z Pr(>|z|)
FACILITY_LOCATION_FMiddle Atlantic     0.07418   1.07700  0.43257  0.171    0.864
FACILITY_LOCATION_FSouth Atlantic      0.09900   1.10407  0.41366  0.239    0.811
FACILITY_LOCATION_FEast North Central  0.19621   1.21678  0.41332  0.475    0.635
FACILITY_LOCATION_FEast South Central  0.55115   1.73525  0.44683  1.233    0.217
FACILITY_LOCATION_FWest North Central  0.52098   1.68368  0.43483  1.198    0.231
FACILITY_LOCATION_FWest South Central  0.56765   1.76411  0.43685  1.299    0.194
FACILITY_LOCATION_FMountain           -0.32226   0.72451  0.55642 -0.579    0.562
FACILITY_LOCATION_FPacific             0.11805   1.12530  0.50497  0.234    0.815

                                      exp(coef) exp(-coef) lower .95 upper .95
FACILITY_LOCATION_FMiddle Atlantic       1.0770     0.9285    0.4613     2.514
FACILITY_LOCATION_FSouth Atlantic        1.1041     0.9057    0.4908     2.484
FACILITY_LOCATION_FEast North Central    1.2168     0.8218    0.5412     2.735
FACILITY_LOCATION_FEast South Central    1.7352     0.5763    0.7228     4.166
FACILITY_LOCATION_FWest North Central    1.6837     0.5939    0.7180     3.948
FACILITY_LOCATION_FWest South Central    1.7641     0.5669    0.7493     4.153
FACILITY_LOCATION_FMountain              0.7245     1.3802    0.2435     2.156
FACILITY_LOCATION_FPacific               1.1253     0.8887    0.4182     3.028

Concordance= 0.557  (se = 0.023 )
Rsquare= 0.015   (max possible= 0.961 )
Likelihood ratio test= 9.48  on 8 df,   p=0.3035
Wald test            = 9.52  on 8 df,   p=0.3002
Score (logrank) test = 9.75  on 8 df,   p=0.283
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  FACILITY_LOCATION_F

Facility Geography

uni_var(test_var = "FACILITY_GEOGRAPHY", data_imp = data)
_________________________________________________
   
## FACILITY_GEOGRAPHY
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_GEOGRAPHY, data = data)

   35 observations deleted due to missingness 
                               n events median 0.95LCL 0.95UCL
FACILITY_GEOGRAPHY=Northeast 124     30     NA     138      NA
FACILITY_GEOGRAPHY=South     203     57    134     115      NA
FACILITY_GEOGRAPHY=Midwest   237     77    133     108      NA
FACILITY_GEOGRAPHY=West       76     15     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_GEOGRAPHY, data = data)

35 observations deleted due to missingness 
                FACILITY_GEOGRAPHY=Northeast 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    100       9    0.924  0.0244        0.877        0.973
   24     88       3    0.895  0.0287        0.841        0.953
   36     78       1    0.884  0.0305        0.826        0.946
   48     64       7    0.802  0.0404        0.727        0.886
   60     58       1    0.789  0.0417        0.712        0.876
  120     14       8    0.621  0.0640        0.508        0.760

                FACILITY_GEOGRAPHY=South 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    169      12    0.938  0.0174        0.904        0.972
   24    143       8    0.891  0.0232        0.847        0.937
   36    119       7    0.846  0.0276        0.793        0.901
   48    103       4    0.815  0.0305        0.758        0.877
   60     88       6    0.765  0.0348        0.700        0.837
  120     24      16    0.553  0.0548        0.455        0.671

                FACILITY_GEOGRAPHY=Midwest 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    201      19    0.916  0.0184        0.881        0.953
   24    181      11    0.865  0.0230        0.821        0.911
   36    162      12    0.806  0.0269        0.755        0.861
   48    138       8    0.765  0.0293        0.709        0.824
   60    117       5    0.735  0.0311        0.676        0.798
  120     24      19    0.548  0.0466        0.464        0.647

                FACILITY_GEOGRAPHY=West 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     62       6    0.917  0.0324        0.856        0.983
   24     56       1    0.901  0.0356        0.834        0.974
   36     41       4    0.831  0.0471        0.744        0.929
   48     31       0    0.831  0.0471        0.744        0.929
   60     27       1    0.801  0.0540        0.702        0.914
  120     13       3    0.695  0.0739        0.564        0.856




   
## Univariable Cox Proportional Hazard Model for:  FACILITY_GEOGRAPHY

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_GEOGRAPHY, data = data)

  n= 640, number of events= 179 
   (35 observations deleted due to missingness)

                             coef exp(coef) se(coef)      z Pr(>|z|)
FACILITY_GEOGRAPHYSouth    0.1907    1.2101   0.2256  0.845    0.398
FACILITY_GEOGRAPHYMidwest  0.3003    1.3502   0.2156  1.393    0.164
FACILITY_GEOGRAPHYWest    -0.1390    0.8702   0.3165 -0.439    0.661

                          exp(coef) exp(-coef) lower .95 upper .95
FACILITY_GEOGRAPHYSouth      1.2101     0.8264    0.7776     1.883
FACILITY_GEOGRAPHYMidwest    1.3502     0.7406    0.8849     2.060
FACILITY_GEOGRAPHYWest       0.8702     1.1491    0.4680     1.618

Concordance= 0.532  (se = 0.023 )
Rsquare= 0.006   (max possible= 0.961 )
Likelihood ratio test= 3.77  on 3 df,   p=0.2874
Wald test            = 3.6  on 3 df,   p=0.3076
Score (logrank) test = 3.64  on 3 df,   p=0.3033
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  FACILITY_GEOGRAPHY

Age Group

uni_var(test_var = "AGE_F", data_imp = data)
_________________________________________________
   
## AGE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_F, data = data)

                 n events median 0.95LCL 0.95UCL
AGE_F=(0,54]   161     17     NA      NA      NA
AGE_F=(54,64]  149     28  137.0     134      NA
AGE_F=(64,74]  149     30  153.1     115      NA
AGE_F=(74,100] 216    105   73.2      60    93.4

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_F, data = data)

                AGE_F=(0,54] 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    145       3    0.981  0.0109        0.960        1.000
   24    129       4    0.952  0.0176        0.918        0.988
   36    116       5    0.914  0.0239        0.868        0.962
   48    104       1    0.905  0.0251        0.857        0.956
   60     96       0    0.905  0.0251        0.857        0.956
  120     29       4    0.858  0.0334        0.795        0.926

                AGE_F=(54,64] 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    121      11    0.921  0.0227        0.878        0.967
   24    108       3    0.898  0.0259        0.849        0.950
   36     94       2    0.881  0.0282        0.827        0.938
   48     75       4    0.840  0.0334        0.777        0.908
   60     68       1    0.829  0.0347        0.764        0.900
  120     20       4    0.737  0.0552        0.636        0.853

                AGE_F=(64,74] 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    131       5    0.965  0.0152        0.936        0.996
   24    116       6    0.919  0.0235        0.874        0.966
   36    100       3    0.894  0.0268        0.843        0.948
   48     84       3    0.866  0.0306        0.808        0.928
   60     71       0    0.866  0.0306        0.808        0.928
  120     17      12    0.609  0.0719        0.483        0.767

                AGE_F=(74,100] 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    167      27    0.867  0.0238        0.822        0.915
   24    144      10    0.813  0.0279        0.760        0.869
   36    117      15    0.724  0.0329        0.662        0.792
   48     96      11    0.653  0.0361        0.586        0.727
   60     75      12    0.565  0.0391        0.494        0.647
  120     13      26    0.301  0.0446        0.225        0.402




   
## Univariable Cox Proportional Hazard Model for:  AGE_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_F, data = data)

  n= 675, number of events= 180 

                coef exp(coef) se(coef)     z Pr(>|z|)    
AGE_F(54,64]  0.7831    2.1883   0.3086 2.537   0.0112 *  
AGE_F(64,74]  0.8140    2.2570   0.3039 2.678   0.0074 ** 
AGE_F(74,100] 1.9331    6.9109   0.2636 7.334 2.24e-13 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

              exp(coef) exp(-coef) lower .95 upper .95
AGE_F(54,64]      2.188     0.4570     1.195     4.007
AGE_F(64,74]      2.257     0.4431     1.244     4.095
AGE_F(74,100]     6.911     0.1447     4.123    11.585

Concordance= 0.666  (se = 0.023 )
Rsquare= 0.126   (max possible= 0.956 )
Likelihood ratio test= 90.86  on 3 df,   p=0
Wald test            = 82.35  on 3 df,   p=0
Score (logrank) test = 99.33  on 3 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  AGE_F

Age Group

uni_var(test_var = "AGE_40", data_imp = data)
_________________________________________________
   
## AGE_40
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_40, data = data)

                  n events median 0.95LCL 0.95UCL
AGE_40=(0,40]    38      1     NA      NA      NA
AGE_40=(40,100] 637    179    137     133      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_40, data = data)

                AGE_40=(0,40] 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     35       0    1.000  0.0000        1.000            1
   24     32       0    1.000  0.0000        1.000            1
   36     29       1    0.967  0.0328        0.905            1
   48     25       0    0.967  0.0328        0.905            1
   60     22       0    0.967  0.0328        0.905            1
  120      4       0    0.967  0.0328        0.905            1

                AGE_40=(40,100] 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    529      46    0.924  0.0108        0.903        0.946
   24    465      23    0.882  0.0134        0.856        0.909
   36    398      24    0.834  0.0158        0.804        0.866
   48    334      19    0.792  0.0177        0.758        0.828
   60    288      13    0.760  0.0192        0.723        0.798
  120     75      46    0.577  0.0289        0.523        0.637




   
## Univariable Cox Proportional Hazard Model for:  AGE_40

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_40, data = data)

  n= 675, number of events= 180 

                 coef exp(coef) se(coef)     z Pr(>|z|)  
AGE_40(40,100]  2.460    11.708    1.003 2.453   0.0142 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

               exp(coef) exp(-coef) lower .95 upper .95
AGE_40(40,100]     11.71    0.08541      1.64     83.58

Concordance= 0.528  (se = 0.01 )
Rsquare= 0.023   (max possible= 0.956 )
Likelihood ratio test= 15.87  on 1 df,   p=6.772e-05
Wald test            = 6.02  on 1 df,   p=0.01416
Score (logrank) test = 9.74  on 1 df,   p=0.001805
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  AGE_40

Gender

uni_var(test_var = "SEX_F", data_imp = data)
_________________________________________________
   
## SEX_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SEX_F, data = data)

               n events median 0.95LCL 0.95UCL
SEX_F=Male    19      4     NA     101      NA
SEX_F=Female 656    176    153     133      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SEX_F, data = data)

                SEX_F=Male 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     16       1    0.941  0.0571        0.836            1
   24     14       0    0.941  0.0571        0.836            1
   36     13       1    0.874  0.0837        0.724            1
   48     11       0    0.874  0.0837        0.724            1
   60     10       0    0.874  0.0837        0.724            1
  120      3       2    0.546  0.1938        0.273            1

                SEX_F=Female 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    548      45    0.928  0.0103        0.908        0.949
   24    483      23    0.887  0.0129        0.862        0.913
   36    414      24    0.841  0.0153        0.812        0.872
   48    348      19    0.800  0.0172        0.767        0.835
   60    300      13    0.769  0.0186        0.733        0.806
  120     76      44    0.598  0.0283        0.545        0.656




   
## Univariable Cox Proportional Hazard Model for:  SEX_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SEX_F, data = data)

  n= 675, number of events= 180 

              coef exp(coef) se(coef)    z Pr(>|z|)
SEX_FFemale 0.2633    1.3012   0.5059 0.52    0.603

            exp(coef) exp(-coef) lower .95 upper .95
SEX_FFemale     1.301     0.7685    0.4827     3.507

Concordance= 0.506  (se = 0.007 )
Rsquare= 0   (max possible= 0.956 )
Likelihood ratio test= 0.29  on 1 df,   p=0.5872
Wald test            = 0.27  on 1 df,   p=0.6028
Score (logrank) test = 0.27  on 1 df,   p=0.6018
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  SEX_F

RACE_F

uni_var(test_var = "RACE_F", data_imp = data)
_________________________________________________
   
## RACE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RACE_F, data = data)

                   n events median 0.95LCL 0.95UCL
RACE_F=White     570    151  138.2   133.0      NA
RACE_F=Black      77     26   93.4    78.1      NA
RACE_F=Other/Unk  16      2     NA      NA      NA
RACE_F=Asian      12      1     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RACE_F, data = data)

                RACE_F=White 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    479      36    0.934  0.0107        0.913        0.955
   24    424      19    0.895  0.0134        0.869        0.922
   36    364      21    0.849  0.0161        0.818        0.881
   48    301      18    0.804  0.0184        0.769        0.841
   60    256      12    0.770  0.0201        0.732        0.810
  120     70      37    0.601  0.0305        0.544        0.664

                RACE_F=Black 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     62       7    0.904  0.0344        0.839        0.974
   24     52       4    0.843  0.0437        0.761        0.933
   36     45       4    0.776  0.0515        0.681        0.884
   48     41       1    0.758  0.0534        0.660        0.870
   60     38       1    0.739  0.0552        0.639        0.856
  120      3       9    0.412  0.0975        0.259        0.655

                RACE_F=Other/Unk 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     13       2    0.867  0.0878        0.711            1
   24     13       0    0.867  0.0878        0.711            1
   36     11       0    0.867  0.0878        0.711            1
   48     11       0    0.867  0.0878        0.711            1
   60     11       0    0.867  0.0878        0.711            1
  120      5       0    0.867  0.0878        0.711            1

                RACE_F=Asian 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     10       1    0.909  0.0867        0.754            1
   24      8       0    0.909  0.0867        0.754            1
   36      7       0    0.909  0.0867        0.754            1
   48      6       0    0.909  0.0867        0.754            1
   60      5       0    0.909  0.0867        0.754            1
  120      1       0    0.909  0.0867        0.754            1




   
## Univariable Cox Proportional Hazard Model for:  RACE_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RACE_F, data = data)

  n= 675, number of events= 180 

                   coef exp(coef) se(coef)      z Pr(>|z|)  
RACE_FBlack      0.4011    1.4935   0.2132  1.881   0.0599 .
RACE_FOther/Unk -1.1059    0.3309   0.7128 -1.551   0.1208  
RACE_FAsian     -0.9551    0.3848   1.0036 -0.952   0.3412  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                exp(coef) exp(-coef) lower .95 upper .95
RACE_FBlack        1.4935     0.6696   0.98336     2.268
RACE_FOther/Unk    0.3309     3.0221   0.08183     1.338
RACE_FAsian        0.3848     2.5990   0.05382     2.751

Concordance= 0.529  (se = 0.015 )
Rsquare= 0.013   (max possible= 0.956 )
Likelihood ratio test= 8.51  on 3 df,   p=0.03653
Wald test            = 7.2  on 3 df,   p=0.06593
Score (logrank) test = 7.69  on 3 df,   p=0.05295
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  RACE_F

Hispanic

uni_var(test_var = "HISPANIC", data_imp = data)
_________________________________________________
   
## HISPANIC
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ HISPANIC, data = data)

                   n events median 0.95LCL 0.95UCL
HISPANIC=No      587    156    138     133      NA
HISPANIC=Yes      27      3     NA      NA      NA
HISPANIC=Unknown  61     21     NA     128      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ HISPANIC, data = data)

                HISPANIC=No 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    485      40    0.928  0.0110        0.907        0.950
   24    427      21    0.886  0.0138        0.860        0.914
   36    366      22    0.838  0.0164        0.807        0.871
   48    306      16    0.800  0.0183        0.765        0.836
   60    261      12    0.767  0.0198        0.729        0.806
  120     64      39    0.579  0.0318        0.520        0.644

                HISPANIC=Yes 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     24       1    0.962  0.0377        0.890            1
   24     20       0    0.962  0.0377        0.890            1
   36     15       1    0.913  0.0590        0.805            1
   48     10       1    0.830  0.0956        0.663            1
   60      8       0    0.830  0.0956        0.663            1
  120      1       0    0.830  0.0956        0.663            1

                HISPANIC=Unknown 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     55       5    0.918  0.0352        0.851        0.989
   24     50       2    0.883  0.0415        0.806        0.969
   36     46       2    0.847  0.0471        0.759        0.944
   48     43       2    0.810  0.0517        0.715        0.918
   60     41       1    0.790  0.0541        0.691        0.904
  120     14       7    0.643  0.0668        0.525        0.789




   
## Univariable Cox Proportional Hazard Model for:  HISPANIC

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ HISPANIC, data = data)

  n= 675, number of events= 180 

                    coef exp(coef) se(coef)      z Pr(>|z|)
HISPANICYes     -0.68962   0.50177  0.58325 -1.182    0.237
HISPANICUnknown -0.04505   0.95595  0.23390 -0.193    0.847

                exp(coef) exp(-coef) lower .95 upper .95
HISPANICYes        0.5018      1.993    0.1600     1.574
HISPANICUnknown    0.9559      1.046    0.6044     1.512

Concordance= 0.507  (se = 0.014 )
Rsquare= 0.003   (max possible= 0.956 )
Likelihood ratio test= 1.78  on 2 df,   p=0.4117
Wald test            = 1.42  on 2 df,   p=0.4923
Score (logrank) test = 1.47  on 2 df,   p=0.4789
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  HISPANIC

Insurance Status

uni_var(test_var = "INSURANCE_F", data_imp = data)
_________________________________________________
   
## INSURANCE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ INSURANCE_F, data = data)

                               n events median 0.95LCL 0.95UCL
INSURANCE_F=Private          273     35     NA      NA      NA
INSURANCE_F=None              24     11   78.1    34.2      NA
INSURANCE_F=Medicaid          35      9     NA    89.8      NA
INSURANCE_F=Medicare         320    120   96.6    80.5     133
INSURANCE_F=Other Government   8      1     NA    74.3      NA
INSURANCE_F=Unknown           15      4     NA    73.2      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ INSURANCE_F, data = data)

                INSURANCE_F=Private 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    234       9    0.965  0.0114        0.943        0.988
   24    209       6    0.940  0.0152        0.910        0.970
   36    191       3    0.926  0.0170        0.893        0.959
   48    164       4    0.906  0.0193        0.869        0.944
   60    154       0    0.906  0.0193        0.869        0.944
  120     48      10    0.804  0.0366        0.735        0.879

                INSURANCE_F=None 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     20       3    0.870  0.0702        0.742        1.000
   24     18       2    0.783  0.0860        0.631        0.971
   36     13       3    0.638  0.1030        0.465        0.876
   48     10       1    0.580  0.1087        0.402        0.838
   60      9       0    0.580  0.1087        0.402        0.838
  120      1       2    0.322  0.1563        0.125        0.834

                INSURANCE_F=Medicaid 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     27       3    0.903  0.0533        0.804        1.000
   24     24       1    0.867  0.0622        0.753        0.998
   36     18       4    0.721  0.0844        0.573        0.907
   48     15       0    0.721  0.0844        0.573        0.907
   60     11       0    0.721  0.0844        0.573        0.907
  120      5       1    0.641  0.1065        0.462        0.887

                INSURANCE_F=Medicare 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    264      29    0.905  0.0167        0.873        0.939
   24    229      14    0.855  0.0206        0.815        0.896
   36    190      15    0.795  0.0242        0.749        0.844
   48    155      14    0.733  0.0274        0.682        0.789
   60    123      13    0.667  0.0305        0.610        0.730
  120     25      30    0.429  0.0419        0.354        0.520

                INSURANCE_F=Other Government 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      8       0        1       0            1            1
   24      7       0        1       0            1            1
   36      6       0        1       0            1            1
   48      6       0        1       0            1            1
   60      5       0        1       0            1            1

                INSURANCE_F=Unknown 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     11       2    0.867  0.0878        0.711            1
   24     10       0    0.867  0.0878        0.711            1
   36      9       0    0.867  0.0878        0.711            1
   48      9       0    0.867  0.0878        0.711            1
   60      8       0    0.867  0.0878        0.711            1




   
## Univariable Cox Proportional Hazard Model for:  INSURANCE_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ INSURANCE_F, data = data)

  n= 675, number of events= 180 

                               coef exp(coef) se(coef)     z Pr(>|z|)    
INSURANCE_FNone             1.65037   5.20891  0.34718 4.754  2.0e-06 ***
INSURANCE_FMedicaid         0.91602   2.49931  0.37412 2.448   0.0143 *  
INSURANCE_FMedicare         1.33160   3.78708  0.19299 6.900  5.2e-12 ***
INSURANCE_FOther Government 0.03798   1.03871  1.01476 0.037   0.9701    
INSURANCE_FUnknown          1.09154   2.97886  0.52924 2.062   0.0392 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                            exp(coef) exp(-coef) lower .95 upper .95
INSURANCE_FNone                 5.209     0.1920    2.6377    10.286
INSURANCE_FMedicaid             2.499     0.4001    1.2005     5.203
INSURANCE_FMedicare             3.787     0.2641    2.5944     5.528
INSURANCE_FOther Government     1.039     0.9627    0.1421     7.590
INSURANCE_FUnknown              2.979     0.3357    1.0557     8.405

Concordance= 0.642  (se = 0.021 )
Rsquare= 0.09   (max possible= 0.956 )
Likelihood ratio test= 63.7  on 5 df,   p=2.086e-12
Wald test            = 52.65  on 5 df,   p=3.959e-10
Score (logrank) test = 60.62  on 5 df,   p=9.044e-12
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  INSURANCE_F

Overall Survival pre/post-ACA expansion

uni_var(test_var = "EXPN_GROUP", data_imp = no_Excludes)
_________________________________________________
   
## EXPN_GROUP
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EXPN_GROUP, data = no_Excludes)

                            n events median 0.95LCL 0.95UCL
EXPN_GROUP=Post-Expansion  63     10     NA    54.2      NA
EXPN_GROUP=Pre-Expansion  601    172    138   133.0      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EXPN_GROUP, data = no_Excludes)

                EXPN_GROUP=Post-Expansion 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     48       6    0.898  0.0396        0.823        0.979
   24     39       2    0.858  0.0468        0.771        0.955
   36     21       0    0.858  0.0468        0.771        0.955
   48     10       1    0.780  0.0857        0.629        0.968
   60      6       1    0.683  0.1181        0.486        0.958

                EXPN_GROUP=Pre-Expansion 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    505      41    0.929  0.0108        0.908        0.950
   24    446      22    0.886  0.0135        0.860        0.913
   36    396      24    0.837  0.0161        0.806        0.869
   48    342      18    0.798  0.0178        0.764        0.833
   60    299      12    0.768  0.0191        0.732        0.807
  120     81      47    0.588  0.0284        0.535        0.646




   
## Univariable Cox Proportional Hazard Model for:  EXPN_GROUP

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EXPN_GROUP, data = no_Excludes)

  n= 664, number of events= 182 

                            coef exp(coef) se(coef)      z Pr(>|z|)
EXPN_GROUPPre-Expansion -0.08197   0.92130  0.33000 -0.248    0.804

                        exp(coef) exp(-coef) lower .95 upper .95
EXPN_GROUPPre-Expansion    0.9213      1.085    0.4825     1.759

Concordance= 0.504  (se = 0.011 )
Rsquare= 0   (max possible= 0.959 )
Likelihood ratio test= 0.06  on 1 df,   p=0.806
Wald test            = 0.06  on 1 df,   p=0.8038
Score (logrank) test = 0.06  on 1 df,   p=0.8038





   
## Unadjusted Kaplan Meier Overall Survival Curve for:  EXPN_GROUP

Education

uni_var(test_var = "EDUCATION_F", data_imp = data)
_________________________________________________
   
## EDUCATION_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EDUCATION_F, data = data)

   4 observations deleted due to missingness 
                           n events median 0.95LCL 0.95UCL
EDUCATION_F=21% or more   99     32    107    73.2      NA
EDUCATION_F=13 - 20.9%   164     51    116    98.3      NA
EDUCATION_F=7 - 12.9%    222     57     NA   132.9      NA
EDUCATION_F=Less than 7% 186     40    153   136.8      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EDUCATION_F, data = data)

4 observations deleted due to missingness 
                EDUCATION_F=21% or more 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     79       9    0.905  0.0302        0.848        0.966
   24     69       3    0.869  0.0355        0.802        0.941
   36     53       8    0.763  0.0469        0.676        0.861
   48     41       3    0.713  0.0520        0.618        0.823
   60     35       2    0.674  0.0559        0.573        0.793
  120      5       7    0.455  0.0839        0.317        0.653

                EDUCATION_F=13 - 20.9% 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    134      13    0.916  0.0224        0.873        0.961
   24    113       9    0.852  0.0293        0.796        0.911
   36    100       4    0.820  0.0322        0.760        0.886
   48     80       6    0.769  0.0364        0.701        0.843
   60     66       3    0.739  0.0389        0.666        0.819
  120     13      15    0.482  0.0623        0.374        0.620

                EDUCATION_F=7 - 12.9% 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    191      15    0.930  0.0175        0.896        0.965
   24    175       6    0.900  0.0208        0.860        0.942
   36    150       8    0.856  0.0249        0.809        0.906
   48    129       6    0.821  0.0277        0.769        0.877
   60    113       5    0.787  0.0304        0.730        0.849
  120     28      15    0.631  0.0457        0.547        0.727

                EDUCATION_F=Less than 7% 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    157       9    0.949  0.0164        0.918        0.982
   24    138       5    0.917  0.0212        0.877        0.960
   36    123       5    0.883  0.0254        0.834        0.934
   48    108       4    0.853  0.0287        0.798        0.911
   60     95       3    0.828  0.0311        0.770        0.892
  120     33       9    0.716  0.0456        0.632        0.811




   
## Univariable Cox Proportional Hazard Model for:  EDUCATION_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EDUCATION_F, data = data)

  n= 671, number of events= 180 
   (4 observations deleted due to missingness)

                           coef exp(coef) se(coef)      z Pr(>|z|)   
EDUCATION_F13 - 20.9%   -0.1679    0.8454   0.2259 -0.743  0.45730   
EDUCATION_F7 - 12.9%    -0.4740    0.6225   0.2215 -2.140  0.03239 * 
EDUCATION_FLess than 7% -0.7328    0.4806   0.2389 -3.068  0.00216 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                        exp(coef) exp(-coef) lower .95 upper .95
EDUCATION_F13 - 20.9%      0.8454      1.183    0.5430    1.3163
EDUCATION_F7 - 12.9%       0.6225      1.606    0.4033    0.9610
EDUCATION_FLess than 7%    0.4806      2.081    0.3009    0.7675

Concordance= 0.572  (se = 0.023 )
Rsquare= 0.018   (max possible= 0.956 )
Likelihood ratio test= 12.16  on 3 df,   p=0.006867
Wald test            = 12.15  on 3 df,   p=0.006897
Score (logrank) test = 12.46  on 3 df,   p=0.005956
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  EDUCATION_F

Urban/Rural

uni_var(test_var = "U_R_F", data_imp = data)
_________________________________________________
   
## U_R_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ U_R_F, data = data)

   18 observations deleted due to missingness 
              n events median 0.95LCL 0.95UCL
U_R_F=Metro 557    151    137     133      NA
U_R_F=Urban  88     24     NA     110      NA
U_R_F=Rural  12      3     NA      47      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ U_R_F, data = data)

18 observations deleted due to missingness 
                U_R_F=Metro 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    462      42    0.921  0.0117        0.899        0.944
   24    407      19    0.882  0.0143        0.854        0.910
   36    350      19    0.839  0.0167        0.807        0.872
   48    300      12    0.808  0.0182        0.773        0.845
   60    262      12    0.775  0.0199        0.737        0.815
  120     63      39    0.595  0.0310        0.538        0.659

                U_R_F=Urban 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     78       3    0.964  0.0203        0.925        1.000
   24     71       3    0.926  0.0291        0.871        0.985
   36     61       5    0.858  0.0398        0.783        0.940
   48     45       6    0.768  0.0499        0.676        0.872
   60     37       1    0.748  0.0523        0.652        0.858
  120     13       6    0.593  0.0716        0.468        0.751

                U_R_F=Rural 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      9       1    0.900  0.0949        0.732            1
   24      8       0    0.900  0.0949        0.732            1
   36      6       1    0.771  0.1442        0.535            1
   48      4       1    0.617  0.1798        0.349            1
   60      4       0    0.617  0.1798        0.349            1
  120      1       0    0.617  0.1798        0.349            1




   
## Univariable Cox Proportional Hazard Model for:  U_R_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ U_R_F, data = data)

  n= 657, number of events= 178 
   (18 observations deleted due to missingness)

               coef exp(coef) se(coef)      z Pr(>|z|)
U_R_FUrban -0.06257   0.93934  0.21994 -0.284    0.776
U_R_FRural  0.14133   1.15181  0.58359  0.242    0.809

           exp(coef) exp(-coef) lower .95 upper .95
U_R_FUrban    0.9393     1.0646    0.6104     1.446
U_R_FRural    1.1518     0.8682    0.3670     3.615

Concordance= 0.505  (se = 0.015 )
Rsquare= 0   (max possible= 0.957 )
Likelihood ratio test= 0.15  on 2 df,   p=0.9298
Wald test            = 0.15  on 2 df,   p=0.9291
Score (logrank) test = 0.15  on 2 df,   p=0.929
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  U_R_F

Class (treatment at performing facility)

uni_var(test_var = "CLASS_OF_CASE_F", data_imp = data)
_________________________________________________
   
## CLASS_OF_CASE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CLASS_OF_CASE_F, data = data)

                                 n events median 0.95LCL 0.95UCL
CLASS_OF_CASE_F=Other_Facility  41      7     NA    93.4      NA
CLASS_OF_CASE_F=All_Part_Prim  634    173    138   133.0      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CLASS_OF_CASE_F, data = data)

                CLASS_OF_CASE_F=Other_Facility 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     20       5    0.827  0.0714        0.698        0.979
   24     19       0    0.827  0.0714        0.698        0.979
   36     16       0    0.827  0.0714        0.698        0.979
   48     12       1    0.775  0.0835        0.628        0.958
   60     11       0    0.775  0.0835        0.628        0.958
  120      4       1    0.646  0.1370        0.426        0.979

                CLASS_OF_CASE_F=All_Part_Prim 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    544      41    0.933  0.0101        0.914        0.953
   24    478      23    0.892  0.0128        0.867        0.917
   36    411      25    0.843  0.0154        0.813        0.874
   48    347      18    0.804  0.0172        0.771        0.839
   60    299      13    0.772  0.0187        0.737        0.810
  120     75      45    0.594  0.0288        0.540        0.653




   
## Univariable Cox Proportional Hazard Model for:  CLASS_OF_CASE_F
Loglik converged before variable  1 ; beta may be infinite. 
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CLASS_OF_CASE_F, data = data)

  n= 675, number of events= 180 

                                  coef exp(coef)  se(coef)      z Pr(>|z|)
CLASS_OF_CASE_FAll_Part_Prim -0.000415  0.999585  0.385995 -0.001    0.999

                             exp(coef) exp(-coef) lower .95 upper .95
CLASS_OF_CASE_FAll_Part_Prim    0.9996          1    0.4691      2.13

Concordance= 0.506  (se = 0.008 )
Rsquare= 0   (max possible= 0.956 )
Likelihood ratio test= 0  on 1 df,   p=0.9991
Wald test            = 0  on 1 df,   p=0.9991
Score (logrank) test = 0  on 1 df,   p=0.9991
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  CLASS_OF_CASE_F

Year

uni_var(test_var = "YEAR_OF_DIAGNOSIS", data_imp = data)
_________________________________________________
   
## YEAR_OF_DIAGNOSIS
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ YEAR_OF_DIAGNOSIS, data = data)

                        n events median 0.95LCL 0.95UCL
YEAR_OF_DIAGNOSIS=2004 45     16    153   134.1      NA
YEAR_OF_DIAGNOSIS=2005 63     20     NA   137.0      NA
YEAR_OF_DIAGNOSIS=2006 75     33    133    95.3      NA
YEAR_OF_DIAGNOSIS=2007 62     27    115   101.2      NA
YEAR_OF_DIAGNOSIS=2008 42     13     NA      NA      NA
YEAR_OF_DIAGNOSIS=2009 62     14     NA      NA      NA
YEAR_OF_DIAGNOSIS=2010 53     14     NA      NA      NA
YEAR_OF_DIAGNOSIS=2011 64     15     NA    73.8      NA
YEAR_OF_DIAGNOSIS=2012 47      8     NA    66.2      NA
YEAR_OF_DIAGNOSIS=2013 60     11     NA      NA      NA
YEAR_OF_DIAGNOSIS=2014 48      4     NA      NA      NA
YEAR_OF_DIAGNOSIS=2015 54      5     NA    28.4      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ YEAR_OF_DIAGNOSIS, data = data)

                YEAR_OF_DIAGNOSIS=2004 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     36       6    0.862  0.0523        0.766        0.971
   24     31       2    0.811  0.0606        0.700        0.939
   36     28       3    0.732  0.0696        0.608        0.882
   48     28       0    0.732  0.0696        0.608        0.882
   60     26       1    0.706  0.0719        0.578        0.862
  120     16       2    0.650  0.0764        0.516        0.818

                YEAR_OF_DIAGNOSIS=2005 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     58       5    0.921  0.0341        0.856        0.990
   24     49       3    0.870  0.0430        0.790        0.958
   36     45       2    0.833  0.0484        0.744        0.934
   48     43       0    0.833  0.0484        0.744        0.934
   60     43       0    0.833  0.0484        0.744        0.934
  120     28       6    0.702  0.0642        0.586        0.840

                YEAR_OF_DIAGNOSIS=2006 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     67       4    0.946  0.0262        0.896        0.999
   24     60       6    0.861  0.0409        0.784        0.945
   36     57       2    0.832  0.0443        0.749        0.924
   48     53       3    0.788  0.0487        0.698        0.889
   60     52       0    0.788  0.0487        0.698        0.889
  120     26      16    0.524  0.0630        0.414        0.663

                YEAR_OF_DIAGNOSIS=2007 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     56       6    0.903  0.0375        0.833        0.980
   24     54       1    0.887  0.0402        0.812        0.969
   36     48       6    0.789  0.0521        0.693        0.898
   48     43       2    0.756  0.0549        0.655        0.871
   60     40       2    0.720  0.0579        0.615        0.843
  120      9      10    0.496  0.0734        0.371        0.663

                YEAR_OF_DIAGNOSIS=2008 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     37       4    0.902  0.0463        0.816        0.998
   24     36       1    0.878  0.0511        0.783        0.984
   36     34       0    0.878  0.0511        0.783        0.984
   48     31       3    0.801  0.0632        0.686        0.935
   60     30       1    0.775  0.0662        0.655        0.916

                YEAR_OF_DIAGNOSIS=2009 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     54       3    0.950  0.0281        0.896        1.000
   24     51       2    0.914  0.0368        0.845        0.989
   36     47       0    0.914  0.0368        0.845        0.989
   48     40       4    0.834  0.0509        0.740        0.940
   60     36       1    0.812  0.0541        0.713        0.925

                YEAR_OF_DIAGNOSIS=2010 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     41       3    0.941  0.0330        0.879        1.000
   24     40       1    0.918  0.0394        0.844        0.999
   36     37       3    0.849  0.0528        0.752        0.959
   48     34       3    0.780  0.0617        0.668        0.911
   60     31       2    0.735  0.0661        0.616        0.876

                YEAR_OF_DIAGNOSIS=2011 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     59       2    0.968  0.0225        0.925        1.000
   24     56       2    0.935  0.0316        0.875        0.999
   36     50       4    0.867  0.0438        0.785        0.957
   48     45       2    0.830  0.0490        0.740        0.932
   60     39       4    0.753  0.0579        0.647        0.875

                YEAR_OF_DIAGNOSIS=2012 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     41       2    0.953  0.0321        0.893        1.000
   24     39       0    0.953  0.0321        0.893        1.000
   36     34       3    0.879  0.0506        0.786        0.985
   48     30       0    0.879  0.0506        0.786        0.985
   60     13       2    0.821  0.0619        0.708        0.952

                YEAR_OF_DIAGNOSIS=2013 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     45       5    0.907  0.0398        0.832        0.988
   24     37       3    0.841  0.0521        0.744        0.949
   36     34       1    0.817  0.0556        0.715        0.934
   48     12       2    0.749  0.0704        0.622        0.900

                YEAR_OF_DIAGNOSIS=2014 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     32       3    0.931  0.0385        0.859            1
   24     28       1    0.899  0.0487        0.808            1
   36     13       0    0.899  0.0487        0.808            1

                YEAR_OF_DIAGNOSIS=2015 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     38       3    0.937  0.0354        0.870            1
   24     16       1    0.907  0.0448        0.824            1




   
## Univariable Cox Proportional Hazard Model for:  YEAR_OF_DIAGNOSIS
X matrix deemed to be singular; variable 12
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ YEAR_OF_DIAGNOSIS, data = data)

  n= 675, number of events= 180 

                          coef exp(coef) se(coef)      z Pr(>|z|)
YEAR_OF_DIAGNOSIS2005 -0.15599   0.85557  0.34183 -0.456    0.648
YEAR_OF_DIAGNOSIS2006  0.33535   1.39843  0.31703  1.058    0.290
YEAR_OF_DIAGNOSIS2007  0.36223   1.43653  0.33044  1.096    0.273
YEAR_OF_DIAGNOSIS2008  0.01742   1.01758  0.38687  0.045    0.964
YEAR_OF_DIAGNOSIS2009 -0.07927   0.92379  0.38114 -0.208    0.835
YEAR_OF_DIAGNOSIS2010  0.18053   1.19786  0.38188  0.473    0.636
YEAR_OF_DIAGNOSIS2011  0.11601   1.12301  0.37783  0.307    0.759
YEAR_OF_DIAGNOSIS2012 -0.06744   0.93479  0.44973 -0.150    0.881
YEAR_OF_DIAGNOSIS2013  0.32908   1.38968  0.41173  0.799    0.424
YEAR_OF_DIAGNOSIS2014 -0.22881   0.79548  0.57472 -0.398    0.691
YEAR_OF_DIAGNOSIS2015  0.08046   1.08379  0.53305  0.151    0.880
YEAR_OF_DIAGNOSIS2016       NA        NA  0.00000     NA       NA

                      exp(coef) exp(-coef) lower .95 upper .95
YEAR_OF_DIAGNOSIS2005    0.8556     1.1688    0.4378     1.672
YEAR_OF_DIAGNOSIS2006    1.3984     0.7151    0.7512     2.603
YEAR_OF_DIAGNOSIS2007    1.4365     0.6961    0.7517     2.745
YEAR_OF_DIAGNOSIS2008    1.0176     0.9827    0.4767     2.172
YEAR_OF_DIAGNOSIS2009    0.9238     1.0825    0.4377     1.950
YEAR_OF_DIAGNOSIS2010    1.1979     0.8348    0.5667     2.532
YEAR_OF_DIAGNOSIS2011    1.1230     0.8905    0.5355     2.355
YEAR_OF_DIAGNOSIS2012    0.9348     1.0698    0.3872     2.257
YEAR_OF_DIAGNOSIS2013    1.3897     0.7196    0.6201     3.114
YEAR_OF_DIAGNOSIS2014    0.7955     1.2571    0.2579     2.454
YEAR_OF_DIAGNOSIS2015    1.0838     0.9227    0.3812     3.081
YEAR_OF_DIAGNOSIS2016        NA         NA        NA        NA

Concordance= 0.542  (se = 0.023 )
Rsquare= 0.01   (max possible= 0.956 )
Likelihood ratio test= 6.71  on 11 df,   p=0.8218
Wald test            = 6.66  on 11 df,   p=0.8259
Score (logrank) test = 6.75  on 11 df,   p=0.8187
Removed 2 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  YEAR_OF_DIAGNOSIS
This manual palette can handle a maximum of 10 values. You have supplied 12.

Histology

#uni_var(test_var = "HISTOLOGY_F_LIM", data_imp = data)

Grade

uni_var(test_var = "GRADE_F", data_imp = data)
_________________________________________________
   
## GRADE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ GRADE_F, data = data)

                                   n events median 0.95LCL 0.95UCL
GRADE_F=Gr I: Well Diff           22      7  132.9    93.4      NA
GRADE_F=Gr II: Mod Diff           55     14     NA   116.2      NA
GRADE_F=Gr III: Poor Diff        106     39  136.8    73.2      NA
GRADE_F=Gr IV: Undiff/Anaplastic   2      1   35.9      NA      NA
GRADE_F=NA/Unkown                490    119  153.1   134.1      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ GRADE_F, data = data)

                GRADE_F=Gr I: Well Diff 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     21       1    0.955  0.0444        0.871        1.000
   24     18       1    0.904  0.0645        0.786        1.000
   36     15       1    0.854  0.0781        0.714        1.000
   48     14       0    0.854  0.0781        0.714        1.000
   60      9       1    0.793  0.0933        0.630        0.999
  120      2       2    0.566  0.1509        0.336        0.955

                GRADE_F=Gr II: Mod Diff 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     49       4    0.925  0.0361        0.857        0.998
   24     44       1    0.905  0.0404        0.829        0.988
   36     36       4    0.820  0.0547        0.719        0.934
   48     31       0    0.820  0.0547        0.719        0.934
   60     27       0    0.820  0.0547        0.719        0.934
  120      5       5    0.564  0.1223        0.369        0.863

                GRADE_F=Gr III: Poor Diff 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     92      10    0.903  0.0292        0.848        0.962
   24     81       7    0.833  0.0370        0.763        0.909
   36     71       5    0.779  0.0417        0.702        0.865
   48     57       7    0.701  0.0469        0.615        0.799
   60     48       3    0.663  0.0492        0.573        0.767
  120     13       6    0.557  0.0574        0.455        0.681

                GRADE_F=Gr IV: Undiff/Anaplastic 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      2       0        1       0            1            1
   24      1       0        1       0            1            1

                GRADE_F=NA/Unkown 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    400      31    0.933  0.0116        0.911        0.956
   24    353      14    0.899  0.0143        0.871        0.928
   36    305      14    0.862  0.0169        0.829        0.895
   48    257      12    0.826  0.0191        0.789        0.864
   60    226       9    0.795  0.0210        0.755        0.837
  120     59      33    0.613  0.0336        0.551        0.683




   
## Univariable Cox Proportional Hazard Model for:  GRADE_F
X matrix deemed to be singular; variable 4 5 6 7
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ GRADE_F, data = data)

  n= 675, number of events= 180 

                                   coef exp(coef) se(coef)      z Pr(>|z|)
GRADE_FGr II: Mod Diff          -0.1592    0.8528   0.4640 -0.343    0.731
GRADE_FGr III: Poor Diff         0.2431    1.2752   0.4112  0.591    0.554
GRADE_FGr IV: Undiff/Anaplastic  1.2910    3.6363   1.0721  1.204    0.229
GRADE_F5                             NA        NA   0.0000     NA       NA
GRADE_F6                             NA        NA   0.0000     NA       NA
GRADE_F7                             NA        NA   0.0000     NA       NA
GRADE_F8                             NA        NA   0.0000     NA       NA
GRADE_FNA/Unkown                -0.1821    0.8335   0.3898 -0.467    0.640

                                exp(coef) exp(-coef) lower .95 upper .95
GRADE_FGr II: Mod Diff             0.8528     1.1726    0.3435     2.117
GRADE_FGr III: Poor Diff           1.2752     0.7842    0.5696     2.855
GRADE_FGr IV: Undiff/Anaplastic    3.6363     0.2750    0.4447    29.735
GRADE_F5                               NA         NA        NA        NA
GRADE_F6                               NA         NA        NA        NA
GRADE_F7                               NA         NA        NA        NA
GRADE_F8                               NA         NA        NA        NA
GRADE_FNA/Unkown                   0.8335     1.1998    0.3882     1.790

Concordance= 0.543  (se = 0.019 )
Rsquare= 0.009   (max possible= 0.956 )
Likelihood ratio test= 6.3  on 4 df,   p=0.178
Wald test            = 7.29  on 4 df,   p=0.1215
Score (logrank) test = 7.7  on 4 df,   p=0.1032
Removed 5 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  GRADE_F

Clinical T Stage

#uni_var(test_var = "TNM_CLIN_T", data_imp = data)

Clinical N Stage

#uni_var(test_var = "TNM_CLIN_N", data_imp = data)

Clinical M Stage

#uni_var(test_var = "TNM_CLIN_M", data_imp = data)

Clinical Stage Group

uni_var(test_var = "TNM_CLIN_STAGE_GROUP", data_imp = data)
_________________________________________________
   
## TNM_CLIN_STAGE_GROUP
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_STAGE_GROUP, data = data)

                          n events median 0.95LCL 0.95UCL
TNM_CLIN_STAGE_GROUP=0  333     74     NA  138.22      NA
TNM_CLIN_STAGE_GROUP=1   39      5     NA  132.99      NA
TNM_CLIN_STAGE_GROUP=1A  32      3     NA      NA      NA
TNM_CLIN_STAGE_GROUP=1B   1      0     NA      NA      NA
TNM_CLIN_STAGE_GROUP=2A  23      6     NA      NA      NA
TNM_CLIN_STAGE_GROUP=2B  13      5  71.03   69.68      NA
TNM_CLIN_STAGE_GROUP=3    2      1   7.33    7.33      NA
TNM_CLIN_STAGE_GROUP=3A   7      3 116.17   50.99      NA
TNM_CLIN_STAGE_GROUP=3B  12      9  24.42    4.50      NA
TNM_CLIN_STAGE_GROUP=3C   3      2  42.15    4.21      NA
TNM_CLIN_STAGE_GROUP=4   27     22  14.00    8.34    26.6
TNM_CLIN_STAGE_GROUP=99 183     50 153.13  134.08      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_STAGE_GROUP, data = data)

                TNM_CLIN_STAGE_GROUP=0 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    280      10    0.968  0.0101        0.948        0.988
   24    250      11    0.928  0.0151        0.899        0.958
   36    218       7    0.901  0.0179        0.867        0.937
   48    175      14    0.839  0.0231        0.795        0.885
   60    143       6    0.808  0.0255        0.759        0.859
  120     25      25    0.585  0.0464        0.501        0.683

                TNM_CLIN_STAGE_GROUP=1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     38       0    1.000  0.0000        1.000            1
   24     34       0    1.000  0.0000        1.000            1
   36     32       0    1.000  0.0000        1.000            1
   48     26       0    1.000  0.0000        1.000            1
   60     24       2    0.923  0.0523        0.826            1
  120     12       1    0.877  0.0670        0.755            1

                TNM_CLIN_STAGE_GROUP=1A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     30       0    1.000  0.0000        1.000            1
   24     26       0    1.000  0.0000        1.000            1
   36     21       2    0.916  0.0567        0.812            1
   48     19       0    0.916  0.0567        0.812            1
   60     16       0    0.916  0.0567        0.812            1

                TNM_CLIN_STAGE_GROUP=1B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      1       0        1       0            1            1
   24      1       0        1       0            1            1
   36      1       0        1       0            1            1
   48      1       0        1       0            1            1
   60      1       0        1       0            1            1

                TNM_CLIN_STAGE_GROUP=2A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     19       4    0.826  0.0790        0.685        0.996
   24     15       1    0.774  0.0894        0.618        0.971
   36     12       1    0.719  0.0986        0.550        0.941
   48     11       0    0.719  0.0986        0.550        0.941
   60      8       0    0.719  0.0986        0.550        0.941
  120      2       0    0.719  0.0986        0.550        0.941

                TNM_CLIN_STAGE_GROUP=2B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     11       1    0.923  0.0739        0.789            1
   24     10       1    0.839  0.1045        0.657            1
   36      8       1    0.746  0.1279        0.533            1
   48      7       0    0.746  0.1279        0.533            1
   60      6       0    0.746  0.1279        0.533            1

                TNM_CLIN_STAGE_GROUP=3 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      1       1      0.5   0.354        0.125            1
   24      1       0      0.5   0.354        0.125            1
   36      1       0      0.5   0.354        0.125            1

                TNM_CLIN_STAGE_GROUP=3A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      6       1    0.857   0.132       0.6334            1
   24      6       0    0.857   0.132       0.6334            1
   36      6       0    0.857   0.132       0.6334            1
   48      5       0    0.857   0.132       0.6334            1
   60      4       1    0.686   0.186       0.4026            1
  120      1       1    0.343   0.260       0.0777            1

                TNM_CLIN_STAGE_GROUP=3B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      7       5    0.583   0.142        0.362        0.941
   24      6       1    0.500   0.144        0.284        0.880
   36      5       1    0.417   0.142        0.213        0.814
   48      3       1    0.333   0.136        0.150        0.742
   60      3       0    0.333   0.136        0.150        0.742

                TNM_CLIN_STAGE_GROUP=3C 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      2       1    0.667   0.272       0.2995            1
   24      2       0    0.667   0.272       0.2995            1
   36      2       0    0.667   0.272       0.2995            1
   48      1       1    0.333   0.272       0.0673            1
   60      1       0    0.333   0.272       0.0673            1

                TNM_CLIN_STAGE_GROUP=4 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     15      11    0.578  0.0968       0.4160        0.802
   24      7       7    0.292  0.0914       0.1582        0.539
   36      3       4    0.125  0.0672       0.0437        0.359
   48      1       0    0.125  0.0672       0.0437        0.359
   60      1       0    0.125  0.0672       0.0437        0.359

                TNM_CLIN_STAGE_GROUP=99 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    154      12    0.932  0.0189        0.896        0.970
   24    139       2    0.920  0.0206        0.880        0.961
   36    118       9    0.857  0.0278        0.805        0.914
   48    110       3    0.835  0.0298        0.779        0.896
   60    103       4    0.804  0.0326        0.743        0.871
  120     39      15    0.646  0.0457        0.562        0.742




   
## Univariable Cox Proportional Hazard Model for:  TNM_CLIN_STAGE_GROUP
Loglik converged before variable  3 ; beta may be infinite. X matrix deemed to be singular; variable 4 5 8 14 15 16 17 18 19
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_STAGE_GROUP, data = data)

  n= 675, number of events= 180 

                              coef  exp(coef)   se(coef)      z Pr(>|z|)    
TNM_CLIN_STAGE_GROUP1   -9.685e-01  3.797e-01  4.634e-01 -2.090   0.0366 *  
TNM_CLIN_STAGE_GROUP1A  -7.286e-01  4.826e-01  5.899e-01 -1.235   0.2168    
TNM_CLIN_STAGE_GROUP1B  -1.306e+01  2.128e-06  1.445e+03 -0.009   0.9928    
TNM_CLIN_STAGE_GROUP1C          NA         NA  0.000e+00     NA       NA    
TNM_CLIN_STAGE_GROUP2           NA         NA  0.000e+00     NA       NA    
TNM_CLIN_STAGE_GROUP2A   2.323e-01  1.261e+00  4.249e-01  0.547   0.5846    
TNM_CLIN_STAGE_GROUP2B   7.265e-01  2.068e+00  4.631e-01  1.569   0.1167    
TNM_CLIN_STAGE_GROUP2C          NA         NA  0.000e+00     NA       NA    
TNM_CLIN_STAGE_GROUP3    1.660e+00  5.260e+00  1.009e+00  1.645   0.1001    
TNM_CLIN_STAGE_GROUP3A   5.081e-01  1.662e+00  5.896e-01  0.862   0.3888    
TNM_CLIN_STAGE_GROUP3B   1.745e+00  5.729e+00  3.542e-01  4.928 8.29e-07 ***
TNM_CLIN_STAGE_GROUP3C   1.136e+00  3.115e+00  7.176e-01  1.583   0.1133    
TNM_CLIN_STAGE_GROUP4    2.501e+00  1.220e+01  2.554e-01  9.793  < 2e-16 ***
TNM_CLIN_STAGE_GROUP4A          NA         NA  0.000e+00     NA       NA    
TNM_CLIN_STAGE_GROUP4A1         NA         NA  0.000e+00     NA       NA    
TNM_CLIN_STAGE_GROUP4A2         NA         NA  0.000e+00     NA       NA    
TNM_CLIN_STAGE_GROUP4B          NA         NA  0.000e+00     NA       NA    
TNM_CLIN_STAGE_GROUP4C          NA         NA  0.000e+00     NA       NA    
TNM_CLIN_STAGE_GROUPN_A         NA         NA  0.000e+00     NA       NA    
TNM_CLIN_STAGE_GROUP99  -5.359e-02  9.478e-01  1.853e-01 -0.289   0.7725    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                        exp(coef) exp(-coef) lower .95 upper .95
TNM_CLIN_STAGE_GROUP1   3.797e-01  2.634e+00    0.1531    0.9416
TNM_CLIN_STAGE_GROUP1A  4.826e-01  2.072e+00    0.1519    1.5334
TNM_CLIN_STAGE_GROUP1B  2.128e-06  4.699e+05    0.0000       Inf
TNM_CLIN_STAGE_GROUP1C         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP2          NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP2A  1.261e+00  7.927e-01    0.5485    2.9010
TNM_CLIN_STAGE_GROUP2B  2.068e+00  4.836e-01    0.8343    5.1252
TNM_CLIN_STAGE_GROUP2C         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP3   5.260e+00  1.901e-01    0.7273   38.0386
TNM_CLIN_STAGE_GROUP3A  1.662e+00  6.016e-01    0.5234    5.2791
TNM_CLIN_STAGE_GROUP3B  5.729e+00  1.746e-01    2.8614   11.4690
TNM_CLIN_STAGE_GROUP3C  3.115e+00  3.210e-01    0.7632   12.7132
TNM_CLIN_STAGE_GROUP4   1.220e+01  8.199e-02    7.3934   20.1202
TNM_CLIN_STAGE_GROUP4A         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP4A1        NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP4A2        NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP4B         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP4C         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUPN_A        NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP99  9.478e-01  1.055e+00    0.6591    1.3630

Concordance= 0.655  (se = 0.022 )
Rsquare= 0.132   (max possible= 0.956 )
Likelihood ratio test= 95.51  on 11 df,   p=1.332e-15
Wald test            = 134.5  on 11 df,   p=0
Score (logrank) test = 211.1  on 11 df,   p=0
Transformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 10 rows containing missing values (geom_errorbar).Removed 21 rows containing missing values (geom_text).Removed 21 rows containing missing values (geom_text).Removed 21 rows containing missing values (geom_text).Removed 21 rows containing missing values (geom_text).Removed 21 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_CLIN_STAGE_GROUP
This manual palette can handle a maximum of 10 values. You have supplied 12.

Pathologic T Stage

uni_var(test_var = "TNM_PATH_T", data_imp = data)
_________________________________________________
   
## TNM_PATH_T
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_T, data = data)

   58 observations deleted due to missingness 
                  n events median 0.95LCL 0.95UCL
TNM_PATH_T=p0     8      0     NA      NA      NA
TNM_PATH_T=p1    13      2     NA   66.07      NA
TNM_PATH_T=p1A   17      1     NA      NA      NA
TNM_PATH_T=p1B   11      3     NA   71.85      NA
TNM_PATH_T=p1C   24      4 136.77  136.77      NA
TNM_PATH_T=p1MI   7      1     NA      NA      NA
TNM_PATH_T=p2    12      5     NA   26.87      NA
TNM_PATH_T=p3     2      0     NA      NA      NA
TNM_PATH_T=p4     1      1  14.92      NA      NA
TNM_PATH_T=p4B    5      2     NA    2.10      NA
TNM_PATH_T=p4D    3      3   7.49    4.21      NA
TNM_PATH_T=pIS  261     44     NA  115.48      NA
TNM_PATH_T=pX   253     95 134.08  116.17      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_T, data = data)

58 observations deleted due to missingness 
                TNM_PATH_T=p0 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      6       0        1       0            1            1
   24      6       0        1       0            1            1
   36      6       0        1       0            1            1
   48      4       0        1       0            1            1
   60      4       0        1       0            1            1
  120      1       0        1       0            1            1

                TNM_PATH_T=p1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     11       0    1.000   0.000        1.000            1
   24     10       0    1.000   0.000        1.000            1
   36     10       0    1.000   0.000        1.000            1
   48      7       0    1.000   0.000        1.000            1
   60      6       1    0.857   0.132        0.633            1
  120      1       1    0.643   0.210        0.338            1

                TNM_PATH_T=p1A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     17       0    1.000   0.000        1.000            1
   24     15       0    1.000   0.000        1.000            1
   36     13       0    1.000   0.000        1.000            1
   48     12       0    1.000   0.000        1.000            1
   60     11       0    1.000   0.000        1.000            1
  120      4       1    0.875   0.117        0.673            1

                TNM_PATH_T=p1B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     10       0      1.0  0.0000        1.000            1
   24      9       1      0.9  0.0949        0.732            1
   36      8       1      0.8  0.1265        0.587            1
   48      7       0      0.8  0.1265        0.587            1
   60      5       0      0.8  0.1265        0.587            1
  120      1       1      0.6  0.1975        0.315            1

                TNM_PATH_T=p1C 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     23       0    1.000   0.000        1.000            1
   24     21       0    1.000   0.000        1.000            1
   36     19       0    1.000   0.000        1.000            1
   48     19       0    1.000   0.000        1.000            1
   60     19       0    1.000   0.000        1.000            1
  120      2       3    0.769   0.117        0.571            1

                TNM_PATH_T=p1MI 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      7       0    1.000   0.000        1.000            1
   24      6       0    1.000   0.000        1.000            1
   36      4       1    0.833   0.152        0.583            1
   48      3       0    0.833   0.152        0.583            1
   60      1       0    0.833   0.152        0.583            1

                TNM_PATH_T=p2 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      9       3    0.750   0.125        0.541        1.000
   24      8       0    0.750   0.125        0.541        1.000
   36      7       1    0.656   0.140        0.432        0.997
   48      7       0    0.656   0.140        0.432        0.997
   60      6       0    0.656   0.140        0.432        0.997
  120      1       1    0.525   0.162        0.286        0.962

                TNM_PATH_T=p3 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      2       0        1       0            1            1
   24      2       0        1       0            1            1
   36      2       0        1       0            1            1
   48      1       0        1       0            1            1
   60      1       0        1       0            1            1

                TNM_PATH_T=p4 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
          12            1            0            1            0            1            1 

                TNM_PATH_T=p4B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      3       2      0.6   0.219        0.293            1
   24      3       0      0.6   0.219        0.293            1
   36      3       0      0.6   0.219        0.293            1
   48      1       0      0.6   0.219        0.293            1
   60      1       0      0.6   0.219        0.293            1

                TNM_PATH_T=p4D 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
     12.0000       1.0000       2.0000       0.3333       0.2722       0.0673       1.0000 

                TNM_PATH_T=pIS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    222       6    0.975 0.00998        0.956        0.995
   24    199       4    0.957 0.01328        0.931        0.984
   36    168       7    0.922 0.01837        0.886        0.958
   48    131       7    0.879 0.02369        0.833        0.926
   60    105       5    0.841 0.02803        0.788        0.898
  120     17      15    0.600 0.06265        0.489        0.736

                TNM_PATH_T=pX 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    215      25    0.898  0.0193        0.861        0.937
   24    190      14    0.837  0.0239        0.792        0.885
   36    170      11    0.787  0.0268        0.736        0.842
   48    154      10    0.740  0.0290        0.686        0.799
   60    142       5    0.716  0.0301        0.659        0.777
  120     52      23    0.567  0.0373        0.498        0.645




   
## Univariable Cox Proportional Hazard Model for:  TNM_PATH_T
Loglik converged before variable  1,12 ; beta may be infinite. X matrix deemed to be singular; variable 8 9 10 11 13 14 16 18 20 22
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_T, data = data)

  n= 617, number of events= 161 
   (58 observations deleted due to missingness)

                     coef  exp(coef)   se(coef)      z Pr(>|z|)    
TNM_PATH_Tp0   -1.565e+01  1.602e-07  1.563e+03 -0.010  0.99202    
TNM_PATH_Tp1   -6.479e-01  5.231e-01  7.152e-01 -0.906  0.36499    
TNM_PATH_Tp1A  -2.046e+00  1.293e-01  1.006e+00 -2.034  0.04198 *  
TNM_PATH_Tp1B  -1.667e-01  8.464e-01  5.872e-01 -0.284  0.77646    
TNM_PATH_Tp1C  -8.101e-01  4.448e-01  5.111e-01 -1.585  0.11295    
TNM_PATH_Tp1MI -4.957e-01  6.091e-01  1.007e+00 -0.492  0.62261    
TNM_PATH_Tp2    3.693e-01  1.447e+00  4.599e-01  0.803  0.42194    
TNM_PATH_Tp2A          NA         NA  0.000e+00     NA       NA    
TNM_PATH_Tp2B          NA         NA  0.000e+00     NA       NA    
TNM_PATH_Tp2C          NA         NA  0.000e+00     NA       NA    
TNM_PATH_Tp2D          NA         NA  0.000e+00     NA       NA    
TNM_PATH_Tp3   -1.568e+01  1.545e-07  3.344e+03 -0.005  0.99626    
TNM_PATH_Tp3A          NA         NA  0.000e+00     NA       NA    
TNM_PATH_Tp3B          NA         NA  0.000e+00     NA       NA    
TNM_PATH_Tp4    2.385e+00  1.086e+01  1.016e+00  2.348  0.01888 *  
TNM_PATH_Tp4A          NA         NA  0.000e+00     NA       NA    
TNM_PATH_Tp4B   6.950e-01  2.004e+00  7.160e-01  0.971  0.33176    
TNM_PATH_Tp4C          NA         NA  0.000e+00     NA       NA    
TNM_PATH_Tp4D   2.867e+00  1.758e+01  6.093e-01  4.705 2.54e-06 ***
TNM_PATH_TpA           NA         NA  0.000e+00     NA       NA    
TNM_PATH_TpIS  -5.420e-01  5.816e-01  1.846e-01 -2.936  0.00332 ** 
TNM_PATH_TpX           NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

               exp(coef) exp(-coef) lower .95 upper .95
TNM_PATH_Tp0   1.602e-07  6.241e+06   0.00000       Inf
TNM_PATH_Tp1   5.231e-01  1.912e+00   0.12876    2.1253
TNM_PATH_Tp1A  1.293e-01  7.733e+00   0.01801    0.9285
TNM_PATH_Tp1B  8.464e-01  1.181e+00   0.26780    2.6754
TNM_PATH_Tp1C  4.448e-01  2.248e+00   0.16337    1.2112
TNM_PATH_Tp1MI 6.091e-01  1.642e+00   0.08459    4.3862
TNM_PATH_Tp2   1.447e+00  6.912e-01   0.58740    3.5632
TNM_PATH_Tp2A         NA         NA        NA        NA
TNM_PATH_Tp2B         NA         NA        NA        NA
TNM_PATH_Tp2C         NA         NA        NA        NA
TNM_PATH_Tp2D         NA         NA        NA        NA
TNM_PATH_Tp3   1.545e-07  6.474e+06   0.00000       Inf
TNM_PATH_Tp3A         NA         NA        NA        NA
TNM_PATH_Tp3B         NA         NA        NA        NA
TNM_PATH_Tp4   1.086e+01  9.212e-02   1.48296   79.4657
TNM_PATH_Tp4A         NA         NA        NA        NA
TNM_PATH_Tp4B  2.004e+00  4.991e-01   0.49242    8.1526
TNM_PATH_Tp4C         NA         NA        NA        NA
TNM_PATH_Tp4D  1.758e+01  5.689e-02   5.32478   58.0210
TNM_PATH_TpA          NA         NA        NA        NA
TNM_PATH_TpIS  5.816e-01  1.719e+00   0.40504    0.8351
TNM_PATH_TpX          NA         NA        NA        NA

Concordance= 0.643  (se = 0.023 )
Rsquare= 0.066   (max possible= 0.95 )
Likelihood ratio test= 41.83  on 12 df,   p=3.557e-05
Wald test            = 48.62  on 12 df,   p=2.439e-06
Score (logrank) test = 90.09  on 12 df,   p=4.752e-14
Transformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 11 rows containing missing values (geom_errorbar).Removed 23 rows containing missing values (geom_text).Removed 23 rows containing missing values (geom_text).Removed 23 rows containing missing values (geom_text).Removed 23 rows containing missing values (geom_text).Removed 23 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_PATH_T
This manual palette can handle a maximum of 10 values. You have supplied 13.

Pathologic N Stage

uni_var(test_var = "TNM_PATH_N", data_imp = data)
_________________________________________________
   
## TNM_PATH_N
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_N, data = data)

   79 observations deleted due to missingness 
                  n events median 0.95LCL 0.95UCL
TNM_PATH_N=p0   233     36     NA      NA      NA
TNM_PATH_N=p0I-  30      3     NA      NA      NA
TNM_PATH_N=p0I+   2      1  73.79      NA      NA
TNM_PATH_N=p1     7      1     NA      NA      NA
TNM_PATH_N=p1A    9      3  75.17   26.74      NA
TNM_PATH_N=p1MI   2      0     NA      NA      NA
TNM_PATH_N=p2     2      1  12.06   12.06      NA
TNM_PATH_N=p2A    2      0     NA      NA      NA
TNM_PATH_N=p3     2      2   7.82    7.49      NA
TNM_PATH_N=p3A    3      2  18.79   10.05      NA
TNM_PATH_N=pX   304    109 134.08  116.17      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_N, data = data)

79 observations deleted due to missingness 
                TNM_PATH_N=p0 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    207       3    0.987 0.00766        0.972        1.000
   24    194       1    0.982 0.00908        0.964        1.000
   36    169       7    0.945 0.01612        0.914        0.977
   48    140       4    0.920 0.02003        0.882        0.960
   60    121       4    0.893 0.02361        0.848        0.940
  120     21      16    0.682 0.05443        0.583        0.797

                TNM_PATH_N=p0I- 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     23       2    0.923  0.0523        0.826            1
   24     19       1    0.877  0.0670        0.755            1
   36     17       0    0.877  0.0670        0.755            1
   48     15       0    0.877  0.0670        0.755            1
   60     10       0    0.877  0.0670        0.755            1

                TNM_PATH_N=p0I+ 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      2       0        1       0            1            1
   24      2       0        1       0            1            1
   36      2       0        1       0            1            1
   48      1       0        1       0            1            1
   60      1       0        1       0            1            1

                TNM_PATH_N=p1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      6       1    0.857   0.132        0.633            1
   24      6       0    0.857   0.132        0.633            1
   36      6       0    0.857   0.132        0.633            1
   48      6       0    0.857   0.132        0.633            1
   60      6       0    0.857   0.132        0.633            1
  120      2       0    0.857   0.132        0.633            1

                TNM_PATH_N=p1A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      8       1    0.889   0.105        0.706            1
   24      7       0    0.889   0.105        0.706            1
   36      5       1    0.741   0.161        0.484            1
   48      4       0    0.741   0.161        0.484            1
   60      3       0    0.741   0.161        0.484            1

                TNM_PATH_N=p1MI 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      2       0        1       0            1            1
   24      2       0        1       0            1            1
   36      2       0        1       0            1            1
   48      2       0        1       0            1            1
   60      1       0        1       0            1            1

                TNM_PATH_N=p2 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      2       0      1.0   0.000        1.000            1
   24      1       1      0.5   0.354        0.125            1
   36      1       0      0.5   0.354        0.125            1

                TNM_PATH_N=p2A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      2       0        1       0            1            1
   24      2       0        1       0            1            1
   36      2       0        1       0            1            1
   48      1       0        1       0            1            1
   60      1       0        1       0            1            1

                TNM_PATH_N=p3 
     time n.risk n.event survival std.err lower 95% CI upper 95% CI

                TNM_PATH_N=p3A 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      12.000        2.000        1.000        0.667        0.272        0.300        1.000 

                TNM_PATH_N=pX 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    254      28    0.904  0.0172        0.871        0.939
   24    220      17    0.842  0.0217        0.800        0.885
   36    193      13    0.790  0.0247        0.743        0.840
   48    169      11    0.744  0.0269        0.693        0.798
   60    154       7    0.712  0.0283        0.659        0.770
  120     56      26    0.556  0.0356        0.491        0.631




   
## Univariable Cox Proportional Hazard Model for:  TNM_PATH_N
Loglik converged before variable  10,12 ; beta may be infinite. X matrix deemed to be singular; variable 4 5 8 9 13 14 17 18 19 20
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_N, data = data)

  n= 596, number of events= 158 
   (79 observations deleted due to missingness)

                     coef  exp(coef)   se(coef)      z Pr(>|z|)    
TNM_PATH_Np0   -7.948e-01  4.517e-01  1.931e-01 -4.115 3.87e-05 ***
TNM_PATH_Np0I- -8.792e-01  4.151e-01  5.868e-01 -1.498 0.134076    
TNM_PATH_Np0I+  5.249e-01  1.690e+00  1.006e+00  0.522 0.601899    
TNM_PATH_Np0M-         NA         NA  0.000e+00     NA       NA    
TNM_PATH_Np0M+         NA         NA  0.000e+00     NA       NA    
TNM_PATH_Np1   -1.091e+00  3.358e-01  1.005e+00 -1.086 0.277440    
TNM_PATH_Np1A   2.567e-01  1.293e+00  5.866e-01  0.438 0.661700    
TNM_PATH_Np1B          NA         NA  0.000e+00     NA       NA    
TNM_PATH_Np1C          NA         NA  0.000e+00     NA       NA    
TNM_PATH_Np1MI -1.466e+01  4.280e-07  1.984e+03 -0.007 0.994102    
TNM_PATH_Np2    1.150e+00  3.157e+00  1.008e+00  1.140 0.254168    
TNM_PATH_Np2A  -1.466e+01  4.287e-07  1.981e+03 -0.007 0.994094    
TNM_PATH_Np2B          NA         NA  0.000e+00     NA       NA    
TNM_PATH_Np2C          NA         NA  0.000e+00     NA       NA    
TNM_PATH_Np3    2.826e+00  1.688e+01  7.363e-01  3.838 0.000124 ***
TNM_PATH_Np3A   1.862e+00  6.436e+00  7.244e-01  2.570 0.010163 *  
TNM_PATH_Np3B          NA         NA  0.000e+00     NA       NA    
TNM_PATH_Np3C          NA         NA  0.000e+00     NA       NA    
TNM_PATH_Np4           NA         NA  0.000e+00     NA       NA    
TNM_PATH_NpX           NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

               exp(coef) exp(-coef) lower .95 upper .95
TNM_PATH_Np0   4.517e-01  2.214e+00   0.30931    0.6595
TNM_PATH_Np0I- 4.151e-01  2.409e+00   0.13142    1.3112
TNM_PATH_Np0I+ 1.690e+00  5.916e-01   0.23526   12.1436
TNM_PATH_Np0M-        NA         NA        NA        NA
TNM_PATH_Np0M+        NA         NA        NA        NA
TNM_PATH_Np1   3.358e-01  2.978e+00   0.04685    2.4064
TNM_PATH_Np1A  1.293e+00  7.736e-01   0.40942    4.0810
TNM_PATH_Np1B         NA         NA        NA        NA
TNM_PATH_Np1C         NA         NA        NA        NA
TNM_PATH_Np1MI 4.280e-07  2.336e+06   0.00000       Inf
TNM_PATH_Np2   3.157e+00  3.168e-01   0.43764   22.7714
TNM_PATH_Np2A  4.287e-07  2.333e+06   0.00000       Inf
TNM_PATH_Np2B         NA         NA        NA        NA
TNM_PATH_Np2C         NA         NA        NA        NA
TNM_PATH_Np3   1.688e+01  5.924e-02   3.98686   71.4626
TNM_PATH_Np3A  6.436e+00  1.554e-01   1.55593   26.6194
TNM_PATH_Np3B         NA         NA        NA        NA
TNM_PATH_Np3C         NA         NA        NA        NA
TNM_PATH_Np4          NA         NA        NA        NA
TNM_PATH_NpX          NA         NA        NA        NA

Concordance= 0.626  (se = 0.023 )
Rsquare= 0.063   (max possible= 0.952 )
Likelihood ratio test= 38.9  on 10 df,   p=2.645e-05
Wald test            = 46.22  on 10 df,   p=1.307e-06
Score (logrank) test = 73.18  on 10 df,   p=1.076e-11
Transformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 11 rows containing missing values (geom_errorbar).Removed 21 rows containing missing values (geom_text).Removed 21 rows containing missing values (geom_text).Removed 21 rows containing missing values (geom_text).Removed 21 rows containing missing values (geom_text).Removed 21 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_PATH_N
This manual palette can handle a maximum of 10 values. You have supplied 11.

Pathologic M Stage

uni_var(test_var = "TNM_PATH_M", data_imp = data)
_________________________________________________
   
## TNM_PATH_M
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_M, data = data)

   323 observations deleted due to missingness 
                n events median 0.95LCL 0.95UCL
TNM_PATH_M=p1   8      6   18.6      14      NA
TNM_PATH_M=pX 344    118  153.1     134      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_M, data = data)

323 observations deleted due to missingness 
                TNM_PATH_M=p1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      6       2    0.750   0.153       0.5027        1.000
   24      3       3    0.375   0.171       0.1533        0.917
   36      2       1    0.250   0.153       0.0753        0.830
   48      1       0    0.250   0.153       0.0753        0.830
   60      1       0    0.250   0.153       0.0753        0.830

                TNM_PATH_M=pX 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    305      26    0.923  0.0145        0.895        0.952
   24    280      13    0.883  0.0177        0.849        0.918
   36    259      12    0.844  0.0201        0.806        0.884
   48    238      12    0.805  0.0222        0.762        0.849
   60    227       5    0.788  0.0230        0.744        0.834
  120     79      42    0.607  0.0311        0.549        0.671




   
## Univariable Cox Proportional Hazard Model for:  TNM_PATH_M
X matrix deemed to be singular; variable 1 3 4 5 6
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_M, data = data)

  n= 352, number of events= 124 
   (323 observations deleted due to missingness)

                coef exp(coef) se(coef)     z Pr(>|z|)    
TNM_PATH_Mp0      NA        NA   0.0000    NA       NA    
TNM_PATH_Mp1  1.8208    6.1768   0.4287 4.247 2.16e-05 ***
TNM_PATH_Mp1A     NA        NA   0.0000    NA       NA    
TNM_PATH_Mp1B     NA        NA   0.0000    NA       NA    
TNM_PATH_Mp1C     NA        NA   0.0000    NA       NA    
TNM_PATH_MpX      NA        NA   0.0000    NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

              exp(coef) exp(-coef) lower .95 upper .95
TNM_PATH_Mp0         NA         NA        NA        NA
TNM_PATH_Mp1      6.177     0.1619     2.666     14.31
TNM_PATH_Mp1A        NA         NA        NA        NA
TNM_PATH_Mp1B        NA         NA        NA        NA
TNM_PATH_Mp1C        NA         NA        NA        NA
TNM_PATH_MpX         NA         NA        NA        NA

Concordance= 0.526  (se = 0.005 )
Rsquare= 0.032   (max possible= 0.977 )
Likelihood ratio test= 11.38  on 1 df,   p=0.0007442
Wald test            = 18.04  on 1 df,   p=2.162e-05
Score (logrank) test = 23.59  on 1 df,   p=1.193e-06
Removed 6 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_PATH_M

Pathologic Stage Group

uni_var(test_var = "TNM_PATH_STAGE_GROUP", data_imp = data)
_________________________________________________
   
## TNM_PATH_STAGE_GROUP
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_STAGE_GROUP, data = data)

   40 observations deleted due to missingness 
                          n events median 0.95LCL 0.95UCL
TNM_PATH_STAGE_GROUP=0  298     58  153.1  138.22      NA
TNM_PATH_STAGE_GROUP=1   55     10     NA  136.77      NA
TNM_PATH_STAGE_GROUP=1A  26      2     NA   73.79      NA
TNM_PATH_STAGE_GROUP=1B   2      0     NA      NA      NA
TNM_PATH_STAGE_GROUP=2    3      0     NA      NA      NA
TNM_PATH_STAGE_GROUP=2A  21      6     NA      NA      NA
TNM_PATH_STAGE_GROUP=2B   8      2  132.9   75.17      NA
TNM_PATH_STAGE_GROUP=3A   4      2   93.6   71.03      NA
TNM_PATH_STAGE_GROUP=3B   6      2     NA   19.65      NA
TNM_PATH_STAGE_GROUP=3C   5      4   10.1    8.15      NA
TNM_PATH_STAGE_GROUP=4    9      7   14.9   12.06      NA
TNM_PATH_STAGE_GROUP=99 198     77  107.9   83.94      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_STAGE_GROUP, data = data)

40 observations deleted due to missingness 
                TNM_PATH_STAGE_GROUP=0 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    258       7    0.975 0.00936        0.957        0.993
   24    239       3    0.963 0.01139        0.941        0.986
   36    209       7    0.934 0.01556        0.904        0.965
   48    172       9    0.890 0.02059        0.851        0.931
   60    144       7    0.850 0.02452        0.804        0.900
  120     33      22    0.636 0.04660        0.551        0.734

                TNM_PATH_STAGE_GROUP=1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     53       0    1.000  0.0000        1.000        1.000
   24     51       0    1.000  0.0000        1.000        1.000
   36     50       0    1.000  0.0000        1.000        1.000
   48     43       1    0.979  0.0206        0.940        1.000
   60     40       2    0.934  0.0371        0.864        1.000
  120     15       6    0.768  0.0694        0.643        0.917

                TNM_PATH_STAGE_GROUP=1A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     24       0     1.00  0.0000        1.000            1
   24     21       0     1.00  0.0000        1.000            1
   36     16       1     0.95  0.0487        0.859            1
   48     15       0     0.95  0.0487        0.859            1
   60     11       0     0.95  0.0487        0.859            1

                TNM_PATH_STAGE_GROUP=1B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      2       0        1       0            1            1
   24      2       0        1       0            1            1
   36      2       0        1       0            1            1
   48      2       0        1       0            1            1
   60      1       0        1       0            1            1

                TNM_PATH_STAGE_GROUP=2 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      3       0        1       0            1            1
   24      3       0        1       0            1            1
   36      3       0        1       0            1            1
   48      3       0        1       0            1            1
   60      3       0        1       0            1            1
  120      1       0        1       0            1            1

                TNM_PATH_STAGE_GROUP=2A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     19       2    0.905  0.0641        0.788        1.000
   24     17       1    0.854  0.0778        0.715        1.000
   36     13       3    0.694  0.1046        0.517        0.933
   48     13       0    0.694  0.1046        0.517        0.933
   60     12       0    0.694  0.1046        0.517        0.933
  120      4       0    0.694  0.1046        0.517        0.933

                TNM_PATH_STAGE_GROUP=2B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      8       0    1.000   0.000          1.0            1
   24      6       0    1.000   0.000          1.0            1
   36      6       0    1.000   0.000          1.0            1
   48      5       0    1.000   0.000          1.0            1
   60      5       0    1.000   0.000          1.0            1
  120      1       1    0.667   0.272          0.3            1

                TNM_PATH_STAGE_GROUP=3A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      4       0        1       0            1            1
   24      4       0        1       0            1            1
   36      4       0        1       0            1            1
   48      3       0        1       0            1            1
   60      3       0        1       0            1            1

                TNM_PATH_STAGE_GROUP=3B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      5       1    0.833   0.152        0.583            1
   24      4       1    0.667   0.192        0.379            1
   36      4       0    0.667   0.192        0.379            1
   48      2       0    0.667   0.192        0.379            1
   60      2       0    0.667   0.192        0.379            1

                TNM_PATH_STAGE_GROUP=3C 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      12.000        2.000        3.000        0.400        0.219        0.137        1.000 

                TNM_PATH_STAGE_GROUP=4 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      7       2    0.778   0.139       0.5485        1.000
   24      3       4    0.333   0.157       0.1323        0.840
   36      2       1    0.222   0.139       0.0655        0.754
   48      1       0    0.222   0.139       0.0655        0.754
   60      1       0    0.222   0.139       0.0655        0.754

                TNM_PATH_STAGE_GROUP=99 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    149      29    0.846  0.0264        0.795        0.899
   24    125      11    0.780  0.0310        0.721        0.843
   36    104      10    0.713  0.0348        0.648        0.784
   48     90       7    0.664  0.0370        0.595        0.740
   60     80       4    0.633  0.0383        0.562        0.713
  120     25      13    0.498  0.0457        0.416        0.596




   
## Univariable Cox Proportional Hazard Model for:  TNM_PATH_STAGE_GROUP
Loglik converged before variable  3,5 ; beta may be infinite. X matrix deemed to be singular; variable 4 8 9 14 15 16 17 18
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_STAGE_GROUP, data = data)

  n= 635, number of events= 170 
   (40 observations deleted due to missingness)

                              coef  exp(coef)   se(coef)      z Pr(>|z|)    
TNM_PATH_STAGE_GROUP1   -4.050e-01  6.670e-01  3.430e-01 -1.181    0.238    
TNM_PATH_STAGE_GROUP1A  -6.421e-01  5.262e-01  7.204e-01 -0.891    0.373    
TNM_PATH_STAGE_GROUP1B  -1.555e+01  1.772e-07  4.039e+03 -0.004    0.997    
TNM_PATH_STAGE_GROUP1C          NA         NA  0.000e+00     NA       NA    
TNM_PATH_STAGE_GROUP2   -1.545e+01  1.948e-07  2.619e+03 -0.006    0.995    
TNM_PATH_STAGE_GROUP2A   2.827e-01  1.327e+00  4.294e-01  0.658    0.510    
TNM_PATH_STAGE_GROUP2B   2.345e-01  1.264e+00  7.196e-01  0.326    0.745    
TNM_PATH_STAGE_GROUP2C          NA         NA  0.000e+00     NA       NA    
TNM_PATH_STAGE_GROUP3           NA         NA  0.000e+00     NA       NA    
TNM_PATH_STAGE_GROUP3A   8.199e-01  2.270e+00  7.198e-01  1.139    0.255    
TNM_PATH_STAGE_GROUP3B   7.690e-01  2.158e+00  7.199e-01  1.068    0.285    
TNM_PATH_STAGE_GROUP3C   2.870e+00  1.764e+01  5.315e-01  5.400 6.68e-08 ***
TNM_PATH_STAGE_GROUP4    2.337e+00  1.035e+01  4.062e-01  5.753 8.76e-09 ***
TNM_PATH_STAGE_GROUP4A          NA         NA  0.000e+00     NA       NA    
TNM_PATH_STAGE_GROUP4A1         NA         NA  0.000e+00     NA       NA    
TNM_PATH_STAGE_GROUP4B          NA         NA  0.000e+00     NA       NA    
TNM_PATH_STAGE_GROUP4C          NA         NA  0.000e+00     NA       NA    
TNM_PATH_STAGE_GROUPN_A         NA         NA  0.000e+00     NA       NA    
TNM_PATH_STAGE_GROUP99   8.488e-01  2.337e+00  1.747e-01  4.859 1.18e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                        exp(coef) exp(-coef) lower .95 upper .95
TNM_PATH_STAGE_GROUP1   6.670e-01  1.499e+00    0.3405     1.306
TNM_PATH_STAGE_GROUP1A  5.262e-01  1.900e+00    0.1282     2.160
TNM_PATH_STAGE_GROUP1B  1.772e-07  5.642e+06    0.0000       Inf
TNM_PATH_STAGE_GROUP1C         NA         NA        NA        NA
TNM_PATH_STAGE_GROUP2   1.948e-07  5.132e+06    0.0000       Inf
TNM_PATH_STAGE_GROUP2A  1.327e+00  7.538e-01    0.5718     3.078
TNM_PATH_STAGE_GROUP2B  1.264e+00  7.910e-01    0.3085     5.180
TNM_PATH_STAGE_GROUP2C         NA         NA        NA        NA
TNM_PATH_STAGE_GROUP3          NA         NA        NA        NA
TNM_PATH_STAGE_GROUP3A  2.270e+00  4.405e-01    0.5539     9.305
TNM_PATH_STAGE_GROUP3B  2.158e+00  4.635e-01    0.5262     8.847
TNM_PATH_STAGE_GROUP3C  1.764e+01  5.669e-02    6.2231    49.993
TNM_PATH_STAGE_GROUP4   1.035e+01  9.662e-02    4.6684    22.945
TNM_PATH_STAGE_GROUP4A         NA         NA        NA        NA
TNM_PATH_STAGE_GROUP4A1        NA         NA        NA        NA
TNM_PATH_STAGE_GROUP4B         NA         NA        NA        NA
TNM_PATH_STAGE_GROUP4C         NA         NA        NA        NA
TNM_PATH_STAGE_GROUPN_A        NA         NA        NA        NA
TNM_PATH_STAGE_GROUP99  2.337e+00  4.279e-01    1.6593     3.291

Concordance= 0.691  (se = 0.023 )
Rsquare= 0.099   (max possible= 0.955 )
Likelihood ratio test= 65.86  on 11 df,   p=7.419e-10
Wald test            = 76.85  on 11 df,   p=5.982e-12
Score (logrank) test = 109.2  on 11 df,   p=0
Transformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 9 rows containing missing values (geom_errorbar).Removed 20 rows containing missing values (geom_text).Removed 20 rows containing missing values (geom_text).Removed 20 rows containing missing values (geom_text).Removed 20 rows containing missing values (geom_text).Removed 20 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_PATH_STAGE_GROUP
This manual palette can handle a maximum of 10 values. You have supplied 12.

Margins

uni_var(test_var = "MARGINS", data_imp = data)
_________________________________________________
   
## MARGINS
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ MARGINS, data = data)

                            n events median 0.95LCL 0.95UCL
MARGINS=No Residual       511    106  153.1   137.0      NA
MARGINS=Residual, NOS      10      2     NA      NA      NA
MARGINS=Microscopic Resid  11      4   98.3    23.1      NA
MARGINS=Macroscopic Resid   3      2  112.3   108.5      NA
MARGINS=No surg           122     62   35.5    22.4      51
MARGINS=Unknown            18      4     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ MARGINS, data = data)

                MARGINS=No Residual 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    459      11    0.978  0.0067        0.964        0.991
   24    414       9    0.958  0.0093        0.940        0.976
   36    368      15    0.922  0.0128        0.897        0.947
   48    315      12    0.890  0.0153        0.860        0.920
   60    270      11    0.857  0.0177        0.823        0.892
  120     70      40    0.671  0.0309        0.614        0.735

                MARGINS=Residual, NOS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      8       1    0.889   0.105        0.706            1
   24      7       1    0.778   0.139        0.549            1
   36      5       0    0.778   0.139        0.549            1
   48      5       0    0.778   0.139        0.549            1
   60      5       0    0.778   0.139        0.549            1

                MARGINS=Microscopic Resid 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     11       0    1.000   0.000       1.0000            1
   24      7       3    0.707   0.143       0.4758            1
   36      6       0    0.707   0.143       0.4758            1
   48      5       0    0.707   0.143       0.4758            1
   60      4       0    0.707   0.143       0.4758            1
  120      1       1    0.354   0.260       0.0836            1

                MARGINS=Macroscopic Resid 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      3       0        1       0            1            1
   24      3       0        1       0            1            1
   36      3       0        1       0            1            1
   48      3       0        1       0            1            1
   60      3       0        1       0            1            1

                MARGINS=No surg 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     68      33    0.694  0.0446        0.612        0.787
   24     51      10    0.584  0.0494        0.495        0.689
   36     33       9    0.466  0.0530        0.373        0.582
   48     23       5    0.389  0.0543        0.296        0.512
   60     21       2    0.356  0.0546        0.263        0.480
  120      4       3    0.265  0.0613        0.169        0.417

                MARGINS=Unknown 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     15       1    0.944   0.054        0.844            1
   24     15       0    0.944   0.054        0.844            1
   36     12       1    0.881   0.079        0.739            1
   48      8       2    0.712   0.125        0.504            1
   60      7       0    0.712   0.125        0.504            1
  120      4       0    0.712   0.125        0.504            1




   
## Univariable Cox Proportional Hazard Model for:  MARGINS
X matrix deemed to be singular; variable 4
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ MARGINS, data = data)

  n= 675, number of events= 180 

                            coef exp(coef) se(coef)      z Pr(>|z|)    
MARGINSResidual, NOS     0.23881   1.26974  0.71426  0.334   0.7381    
MARGINSMicroscopic Resid 0.85753   2.35734  0.51000  1.681   0.0927 .  
MARGINSMacroscopic Resid 0.70466   2.02316  0.71470  0.986   0.3242    
MARGINSNot evaluable          NA        NA  0.00000     NA       NA    
MARGINSNo surg           1.75769   5.79900  0.16369 10.738   <2e-16 ***
MARGINSUnknown           0.01931   1.01950  0.51184  0.038   0.9699    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                         exp(coef) exp(-coef) lower .95 upper .95
MARGINSResidual, NOS         1.270     0.7876    0.3131     5.149
MARGINSMicroscopic Resid     2.357     0.4242    0.8676     6.405
MARGINSMacroscopic Resid     2.023     0.4943    0.4985     8.211
MARGINSNot evaluable            NA         NA        NA        NA
MARGINSNo surg               5.799     0.1724    4.2075     7.993
MARGINSUnknown               1.020     0.9809    0.3739     2.780

Concordance= 0.687  (se = 0.016 )
Rsquare= 0.131   (max possible= 0.956 )
Likelihood ratio test= 94.41  on 5 df,   p=0
Wald test            = 117.1  on 5 df,   p=0
Score (logrank) test = 147.3  on 5 df,   p=0
Removed 2 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  MARGINS

Margins Yes/No

#uni_var(test_var = "MARGINS_YN", data_imp = data)

30 Day Readmission

uni_var(test_var = "READM_HOSP_30_DAYS_F", data_imp = data)
_________________________________________________
   
## READM_HOSP_30_DAYS_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ READM_HOSP_30_DAYS_F, data = data)

                                             n events median 0.95LCL 0.95UCL
READM_HOSP_30_DAYS_F=No_Surg_or_No_Readmit 627    168  153.1   132.9      NA
READM_HOSP_30_DAYS_F=Unplan_Readmit_Same    15      5   83.9    74.3      NA
READM_HOSP_30_DAYS_F=Plan_Readmit_Same      18      3  137.0   137.0      NA
READM_HOSP_30_DAYS_F=PlanUnplan_Same         1      1   12.2      NA      NA
READM_HOSP_30_DAYS_F=9                      14      3  136.8      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ READM_HOSP_30_DAYS_F, data = data)

                READM_HOSP_30_DAYS_F=No_Surg_or_No_Readmit 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    522      45    0.925  0.0108        0.904        0.946
   24    457      22    0.884  0.0134        0.858        0.911
   36    388      25    0.833  0.0160        0.802        0.865
   48    324      17    0.794  0.0178        0.760        0.830
   60    282      11    0.766  0.0192        0.729        0.804
  120     73      42    0.585  0.0298        0.530        0.647

                READM_HOSP_30_DAYS_F=Unplan_Readmit_Same 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     12       1    0.929  0.0688        0.803        1.000
   24     12       0    0.929  0.0688        0.803        1.000
   36     11       0    0.929  0.0688        0.803        1.000
   48     11       0    0.929  0.0688        0.803        1.000
   60      7       1    0.836  0.1077        0.649        1.000
  120      1       3    0.418  0.1789        0.181        0.967

                READM_HOSP_30_DAYS_F=Plan_Readmit_Same 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     18       0    1.000  0.0000        1.000            1
   24     18       0    1.000  0.0000        1.000            1
   36     18       0    1.000  0.0000        1.000            1
   48     16       0    1.000  0.0000        1.000            1
   60     13       1    0.938  0.0605        0.826            1
  120      2       1    0.865  0.0890        0.707            1

                READM_HOSP_30_DAYS_F=PlanUnplan_Same 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
          12            1            0            1            0            1            1 

                READM_HOSP_30_DAYS_F=9 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     11       0      1.0   0.000        1.000            1
   24     10       0      1.0   0.000        1.000            1
   36     10       0      1.0   0.000        1.000            1
   48      8       2      0.8   0.126        0.587            1
   60      8       0      0.8   0.126        0.587            1
  120      3       0      0.8   0.126        0.587            1




   
## Univariable Cox Proportional Hazard Model for:  READM_HOSP_30_DAYS_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ READM_HOSP_30_DAYS_F, data = data)

  n= 675, number of events= 180 

                                           coef exp(coef) se(coef)      z Pr(>|z|)  
READM_HOSP_30_DAYS_FUnplan_Readmit_Same  0.1854    1.2038   0.4542  0.408   0.6830  
READM_HOSP_30_DAYS_FPlan_Readmit_Same   -0.8646    0.4212   0.5828 -1.484   0.1379  
READM_HOSP_30_DAYS_FPlanUnplan_Same      2.5567   12.8929   1.0107  2.530   0.0114 *
READM_HOSP_30_DAYS_F9                   -0.3725    0.6890   0.5829 -0.639   0.5228  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                        exp(coef) exp(-coef) lower .95 upper .95
READM_HOSP_30_DAYS_FUnplan_Readmit_Same    1.2038    0.83074    0.4943     2.932
READM_HOSP_30_DAYS_FPlan_Readmit_Same      0.4212    2.37415    0.1344     1.320
READM_HOSP_30_DAYS_FPlanUnplan_Same       12.8929    0.07756    1.7783    93.475
READM_HOSP_30_DAYS_F9                      0.6890    1.45133    0.2198     2.160

Concordance= 0.521  (se = 0.011 )
Rsquare= 0.01   (max possible= 0.956 )
Likelihood ratio test= 6.85  on 4 df,   p=0.1439
Wald test            = 9.24  on 4 df,   p=0.05537
Score (logrank) test = 13.9  on 4 df,   p=0.007615
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  READM_HOSP_30_DAYS_F

Radiation Type

uni_var(test_var = "RX_SUMM_RADIATION_F", data_imp = data)
_________________________________________________
   
## RX_SUMM_RADIATION_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_SUMM_RADIATION_F, data = data)

                                           n events median 0.95LCL 0.95UCL
RX_SUMM_RADIATION_F=None                 465    133    134   110.5      NA
RX_SUMM_RADIATION_F=Beam Radiation       200     44     NA   137.0      NA
RX_SUMM_RADIATION_F=Radioactive Implants   2      1    137      NA      NA
RX_SUMM_RADIATION_F=Unknown                8      2     60    54.2      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_SUMM_RADIATION_F, data = data)

                RX_SUMM_RADIATION_F=None 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    378      37    0.916  0.0132        0.890        0.942
   24    337      14    0.880  0.0158        0.850        0.912
   36    284      20    0.825  0.0190        0.789        0.863
   48    239      13    0.785  0.0211        0.745        0.828
   60    203      10    0.751  0.0228        0.707        0.797
  120     45      34    0.549  0.0361        0.483        0.624

                RX_SUMM_RADIATION_F=Beam Radiation 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    178       9    0.954  0.0150        0.925        0.984
   24    153       9    0.903  0.0217        0.862        0.947
   36    136       5    0.872  0.0250        0.825        0.923
   48    115       6    0.832  0.0287        0.778        0.891
   60    103       1    0.825  0.0295        0.769        0.884
  120     32      12    0.686  0.0457        0.602        0.782

                RX_SUMM_RADIATION_F=Radioactive Implants 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      2       0        1       0            1            1
   24      2       0        1       0            1            1
   36      2       0        1       0            1            1
   48      2       0        1       0            1            1
   60      2       0        1       0            1            1
  120      1       0        1       0            1            1

                RX_SUMM_RADIATION_F=Unknown 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      6       0    1.000   0.000       1.0000            1
   24      5       0    1.000   0.000       1.0000            1
   36      5       0    1.000   0.000       1.0000            1
   48      3       0    1.000   0.000       1.0000            1
   60      2       2    0.333   0.272       0.0673            1
  120      1       0    0.333   0.272       0.0673            1




   
## Univariable Cox Proportional Hazard Model for:  RX_SUMM_RADIATION_F
X matrix deemed to be singular; variable 3 4 5
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_SUMM_RADIATION_F, data = data)

  n= 675, number of events= 180 

                                              coef exp(coef) se(coef)      z Pr(>|z|)  
RX_SUMM_RADIATION_FBeam Radiation         -0.41462   0.66059  0.17430 -2.379   0.0174 *
RX_SUMM_RADIATION_FRadioactive Implants   -0.18012   0.83517  1.00515 -0.179   0.8578  
RX_SUMM_RADIATION_FRadioisotopes                NA        NA  0.00000     NA       NA  
RX_SUMM_RADIATION_FBeam + Imp or Isotopes       NA        NA  0.00000     NA       NA  
RX_SUMM_RADIATION_FRadiation, NOS               NA        NA  0.00000     NA       NA  
RX_SUMM_RADIATION_FUnknown                 0.01881   1.01899  0.71316  0.026   0.9790  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                          exp(coef) exp(-coef) lower .95 upper .95
RX_SUMM_RADIATION_FBeam Radiation            0.6606     1.5138    0.4694    0.9296
RX_SUMM_RADIATION_FRadioactive Implants      0.8352     1.1974    0.1165    5.9890
RX_SUMM_RADIATION_FRadioisotopes                 NA         NA        NA        NA
RX_SUMM_RADIATION_FBeam + Imp or Isotopes        NA         NA        NA        NA
RX_SUMM_RADIATION_FRadiation, NOS                NA         NA        NA        NA
RX_SUMM_RADIATION_FUnknown                   1.0190     0.9814    0.2518    4.1230

Concordance= 0.538  (se = 0.019 )
Rsquare= 0.009   (max possible= 0.956 )
Likelihood ratio test= 6.08  on 3 df,   p=0.108
Wald test            = 5.69  on 3 df,   p=0.1275
Score (logrank) test = 5.77  on 3 df,   p=0.1231
Removed 4 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  RX_SUMM_RADIATION_F

Lymphovascular Invasion

uni_var(test_var = "LYMPH_VASCULAR_INVASION_F", data_imp = data)
_________________________________________________
   
## LYMPH_VASCULAR_INVASION_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ LYMPH_VASCULAR_INVASION_F, data = data)

   349 observations deleted due to missingness 
                                              n events median 0.95LCL 0.95UCL
LYMPH_VASCULAR_INVASION_F=Neg_LymphVasc_Inv 162     21     NA      NA      NA
LYMPH_VASCULAR_INVASION_F=Pos_LumphVasc_Inv  10      1     NA    73.8      NA
LYMPH_VASCULAR_INVASION_F=Unknown           154     35     NA    69.7      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ LYMPH_VASCULAR_INVASION_F, data = data)

349 observations deleted due to missingness 
                LYMPH_VASCULAR_INVASION_F=Neg_LymphVasc_Inv 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    135       4    0.973  0.0133        0.947        0.999
   24    114       2    0.957  0.0171        0.925        0.991
   36     92       8    0.886  0.0289        0.831        0.945
   48     64       3    0.847  0.0353        0.781        0.919
   60     41       4    0.789  0.0434        0.708        0.879

                LYMPH_VASCULAR_INVASION_F=Pos_LumphVasc_Inv 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      9       0        1       0            1            1
   24      9       0        1       0            1            1
   36      8       0        1       0            1            1
   48      6       0        1       0            1            1
   60      5       0        1       0            1            1

                LYMPH_VASCULAR_INVASION_F=Unknown 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    112      14    0.899  0.0257        0.850        0.951
   24     93       6    0.848  0.0315        0.789        0.912
   36     68       4    0.808  0.0360        0.740        0.881
   48     51       4    0.757  0.0418        0.679        0.843
   60     37       4    0.685  0.0509        0.593        0.793




   
## Univariable Cox Proportional Hazard Model for:  LYMPH_VASCULAR_INVASION_F
X matrix deemed to be singular; variable 2
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ LYMPH_VASCULAR_INVASION_F, data = data)

  n= 326, number of events= 57 
   (349 observations deleted due to missingness)

                                              coef exp(coef) se(coef)      z Pr(>|z|)   
LYMPH_VASCULAR_INVASION_FPos_LumphVasc_Inv -0.5599    0.5713   1.0241 -0.547  0.58456   
LYMPH_VASCULAR_INVASION_FN_A                    NA        NA   0.0000     NA       NA   
LYMPH_VASCULAR_INVASION_FUnknown            0.7197    2.0538   0.2762  2.606  0.00916 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                           exp(coef) exp(-coef) lower .95 upper .95
LYMPH_VASCULAR_INVASION_FPos_LumphVasc_Inv    0.5713     1.7505   0.07676     4.252
LYMPH_VASCULAR_INVASION_FN_A                      NA         NA        NA        NA
LYMPH_VASCULAR_INVASION_FUnknown              2.0538     0.4869   1.19534     3.529

Concordance= 0.617  (se = 0.036 )
Rsquare= 0.025   (max possible= 0.838 )
Likelihood ratio test= 8.27  on 2 df,   p=0.01597
Wald test            = 7.79  on 2 df,   p=0.02036
Score (logrank) test = 8.23  on 2 df,   p=0.01635
Removed 2 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  LYMPH_VASCULAR_INVASION_F

Endoscopic/Robotic

uni_var(test_var = "RX_HOSP_SURG_APPR_2010_F", data_imp = data)
_________________________________________________
   
## RX_HOSP_SURG_APPR_2010_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_HOSP_SURG_APPR_2010_F, data = data)

   349 observations deleted due to missingness 
                                        n events median 0.95LCL 0.95UCL
RX_HOSP_SURG_APPR_2010_F=No_Surg       96     29   69.7      47      NA
RX_HOSP_SURG_APPR_2010_F=Endo_Lap       1      1   29.6      NA      NA
RX_HOSP_SURG_APPR_2010_F=Open_Unknown 229     27     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_HOSP_SURG_APPR_2010_F, data = data)

349 observations deleted due to missingness 
                RX_HOSP_SURG_APPR_2010_F=No_Surg 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     61      14    0.834  0.0406        0.758        0.918
   24     44       5    0.755  0.0499        0.663        0.860
   36     31       4    0.678  0.0580        0.573        0.802
   48     20       3    0.601  0.0666        0.484        0.747
   60     14       2    0.535  0.0739        0.408        0.702

                RX_HOSP_SURG_APPR_2010_F=Endo_Lap 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      1       0        1       0            1            1
   24      1       0        1       0            1            1

                RX_HOSP_SURG_APPR_2010_F=Open_Unknown 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    194       4    0.981 0.00944        0.963        1.000
   24    171       3    0.965 0.01288        0.940        0.991
   36    137       7    0.923 0.01989        0.885        0.963
   48    101       4    0.892 0.02472        0.844        0.941
   60     69       6    0.829 0.03376        0.766        0.898




   
## Univariable Cox Proportional Hazard Model for:  RX_HOSP_SURG_APPR_2010_F
X matrix deemed to be singular; variable 1 2 4 6
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_HOSP_SURG_APPR_2010_F, data = data)

  n= 326, number of events= 57 
   (349 observations deleted due to missingness)

                                            coef exp(coef) se(coef)      z Pr(>|z|)    
RX_HOSP_SURG_APPR_2010_FRobot_Assist          NA        NA   0.0000     NA       NA    
RX_HOSP_SURG_APPR_2010_FRobot_to_Open         NA        NA   0.0000     NA       NA    
RX_HOSP_SURG_APPR_2010_FEndo_Lap          1.1126    3.0421   1.0228  1.088    0.277    
RX_HOSP_SURG_APPR_2010_FEndo_Lap_to_Open      NA        NA   0.0000     NA       NA    
RX_HOSP_SURG_APPR_2010_FOpen_Unknown     -1.4090    0.2444   0.2693 -5.231 1.68e-07 ***
RX_HOSP_SURG_APPR_2010_FUnknown               NA        NA   0.0000     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                         exp(coef) exp(-coef) lower .95 upper .95
RX_HOSP_SURG_APPR_2010_FRobot_Assist            NA         NA        NA        NA
RX_HOSP_SURG_APPR_2010_FRobot_to_Open           NA         NA        NA        NA
RX_HOSP_SURG_APPR_2010_FEndo_Lap            3.0421     0.3287    0.4098   22.5814
RX_HOSP_SURG_APPR_2010_FEndo_Lap_to_Open        NA         NA        NA        NA
RX_HOSP_SURG_APPR_2010_FOpen_Unknown        0.2444     4.0919    0.1441    0.4143
RX_HOSP_SURG_APPR_2010_FUnknown                 NA         NA        NA        NA

Concordance= 0.689  (se = 0.03 )
Rsquare= 0.082   (max possible= 0.838 )
Likelihood ratio test= 28.04  on 2 df,   p=8.138e-07
Wald test            = 30.35  on 2 df,   p=2.562e-07
Score (logrank) test = 36.8  on 2 df,   p=1.023e-08
Removed 5 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  RX_HOSP_SURG_APPR_2010_F

Surgery Radiation Sequence

uni_var(test_var = "SURG_RAD_SEQ", data_imp = data)
_________________________________________________
   
## SURG_RAD_SEQ
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURG_RAD_SEQ, data = data)

                             n events median 0.95LCL 0.95UCL
SURG_RAD_SEQ=Surg Alone    363     85  153.1   132.9      NA
SURG_RAD_SEQ=Surg then Rad 181     30     NA   136.8      NA
SURG_RAD_SEQ=Rad Alone      19     14   21.3    11.7      NA
SURG_RAD_SEQ=No Treatment   95     44   36.1    26.6    93.4
SURG_RAD_SEQ=Other          15      6   54.2    54.2      NA
SURG_RAD_SEQ=Rad then Surg   2      1   26.9    26.9      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURG_RAD_SEQ, data = data)

                SURG_RAD_SEQ=Surg Alone 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    322      11    0.968 0.00944        0.950        0.987
   24    293       8    0.943 0.01267        0.919        0.968
   36    257      12    0.903 0.01661        0.871        0.936
   48    222       8    0.873 0.01914        0.836        0.911
   60    187       9    0.836 0.02204        0.794        0.880
  120     44      32    0.612 0.03987        0.539        0.696

                SURG_RAD_SEQ=Surg then Rad 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    166       2    0.989 0.00806        0.973        1.000
   24    145       5    0.957 0.01587        0.927        0.989
   36    131       3    0.937 0.01945        0.899        0.976
   48    110       6    0.892 0.02569        0.843        0.944
   60     99       0    0.892 0.02569        0.843        0.944
  120     29      11    0.746 0.04811        0.658        0.847

                SURG_RAD_SEQ=Rad Alone 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     12       7    0.632   0.111       0.4480        0.890
   24      8       4    0.421   0.113       0.2485        0.713
   36      6       1    0.361   0.112       0.1965        0.663
   48      6       0    0.361   0.112       0.1965        0.663
   60      5       1    0.301   0.108       0.1486        0.609
  120      3       1    0.226   0.104       0.0913        0.557

                SURG_RAD_SEQ=No Treatment 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     55      22    0.734  0.0489        0.644        0.836
   24     42       6    0.645  0.0549        0.545        0.762
   36     27       8    0.505  0.0615        0.398        0.641
   48     17       5    0.402  0.0642        0.294        0.549
   60     16       1    0.378  0.0646        0.270        0.529
  120      1       2    0.284  0.0770        0.167        0.483

                SURG_RAD_SEQ=Other 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      7       4    0.718   0.120       0.5177        0.996
   24      7       0    0.718   0.120       0.5177        0.996
   36      5       0    0.718   0.120       0.5177        0.996
   48      3       0    0.718   0.120       0.5177        0.996
   60      2       2    0.239   0.199       0.0467        1.000
  120      1       0    0.239   0.199       0.0467        1.000

                SURG_RAD_SEQ=Rad then Surg 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      2       0      1.0   0.000        1.000            1
   24      2       0      1.0   0.000        1.000            1
   36      1       1      0.5   0.354        0.125            1
   48      1       0      0.5   0.354        0.125            1
   60      1       0      0.5   0.354        0.125            1
  120      1       0      0.5   0.354        0.125            1




   
## Univariable Cox Proportional Hazard Model for:  SURG_RAD_SEQ
X matrix deemed to be singular; variable 5
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURG_RAD_SEQ, data = data)

  n= 675, number of events= 180 

                                         coef exp(coef) se(coef)      z Pr(>|z|)    
SURG_RAD_SEQSurg then Rad             -0.4087    0.6645   0.2127 -1.921  0.05469 .  
SURG_RAD_SEQRad Alone                  1.5325    4.6296   0.2902  5.280 1.29e-07 ***
SURG_RAD_SEQNo Treatment               1.5731    4.8218   0.1905  8.257  < 2e-16 ***
SURG_RAD_SEQOther                      1.2840    3.6109   0.4243  3.026  0.00247 ** 
SURG_RAD_SEQRad before and after Surg      NA        NA   0.0000     NA       NA    
SURG_RAD_SEQRad then Surg              0.6271    1.8722   1.0067  0.623  0.53331    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                      exp(coef) exp(-coef) lower .95 upper .95
SURG_RAD_SEQSurg then Rad                0.6645     1.5049    0.4379     1.008
SURG_RAD_SEQRad Alone                    4.6296     0.2160    2.6211     8.177
SURG_RAD_SEQNo Treatment                 4.8218     0.2074    3.3192     7.004
SURG_RAD_SEQOther                        3.6109     0.2769    1.5721     8.293
SURG_RAD_SEQRad before and after Surg        NA         NA        NA        NA
SURG_RAD_SEQRad then Surg                1.8722     0.5341    0.2603    13.467

Concordance= 0.695  (se = 0.021 )
Rsquare= 0.127   (max possible= 0.956 )
Likelihood ratio test= 91.79  on 5 df,   p=0
Wald test            = 110.1  on 5 df,   p=0
Score (logrank) test = 137  on 5 df,   p=0
Removed 2 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  SURG_RAD_SEQ

Surgery Yes/No

uni_var(test_var = "SURGERY_YN", data_imp = data)
_________________________________________________
   
## SURGERY_YN
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURGERY_YN, data = data)

                 n events median 0.95LCL 0.95UCL
SURGERY_YN=No  115     58   35.6   24.25    59.7
SURGERY_YN=Ukn   9      4     NA    2.96      NA
SURGERY_YN=Yes 551    118  153.1  136.97      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURGERY_YN, data = data)

                SURGERY_YN=No 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     67      29    0.713  0.0452        0.630        0.808
   24     50      10    0.598  0.0506        0.507        0.706
   36     33       9    0.477  0.0543        0.381        0.596
   48     23       5    0.399  0.0556        0.303        0.524
   60     21       2    0.364  0.0559        0.269        0.492
  120      4       3    0.272  0.0628        0.173        0.427

                SURGERY_YN=Ukn 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      2       4    0.556   0.166         0.31        0.997
   24      2       0    0.556   0.166         0.31        0.997

                SURGERY_YN=Yes 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    495      13    0.975 0.00675        0.962        0.989
   24    445      13    0.949 0.00982        0.930        0.968
   36    394      16    0.913 0.01284        0.888        0.939
   48    336      14    0.879 0.01528        0.850        0.909
   60    289      11    0.849 0.01730        0.815        0.883
  120     75      43    0.656 0.03066        0.599        0.719




   
## Univariable Cox Proportional Hazard Model for:  SURGERY_YN

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURGERY_YN, data = data)

  n= 675, number of events= 180 

                 coef exp(coef) se(coef)       z Pr(>|z|)    
SURGERY_YNUkn  0.7669    2.1532   0.5228   1.467    0.142    
SURGERY_YNYes -1.6801    0.1863   0.1639 -10.250   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

              exp(coef) exp(-coef) lower .95 upper .95
SURGERY_YNUkn    2.1532     0.4644    0.7728     5.999
SURGERY_YNYes    0.1863     5.3663    0.1351     0.257

Concordance= 0.675  (se = 0.013 )
Rsquare= 0.127   (max possible= 0.956 )
Likelihood ratio test= 91.81  on 2 df,   p=0
Wald test            = 116  on 2 df,   p=0
Score (logrank) test = 147.9  on 2 df,   p=0
no non-missing arguments to min; returning Infno non-missing arguments to max; returning -InfTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 1 rows containing missing values (geom_errorbar).Removed 1 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  SURGERY_YN

Radiation Yes/No

uni_var(test_var = "RADIATION_YN", data_imp = data)
_________________________________________________
   
## RADIATION_YN
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RADIATION_YN, data = data)

   7 observations deleted due to missingness 
                   n events median 0.95LCL 0.95UCL
RADIATION_YN=No  466    133    134     110      NA
RADIATION_YN=Yes 202     45     NA     137      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RADIATION_YN, data = data)

7 observations deleted due to missingness 
                RADIATION_YN=No 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    379      37    0.916  0.0132        0.891        0.942
   24    337      14    0.881  0.0157        0.850        0.912
   36    284      20    0.825  0.0190        0.789        0.864
   48    239      13    0.785  0.0211        0.745        0.828
   60    203      10    0.751  0.0228        0.707        0.797
  120     45      34    0.549  0.0361        0.483        0.625

                RADIATION_YN=Yes 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    180       9    0.954  0.0149        0.926        0.984
   24    155       9    0.904  0.0215        0.863        0.947
   36    138       5    0.874  0.0247        0.827        0.924
   48    117       6    0.834  0.0284        0.781        0.892
   60    105       1    0.827  0.0291        0.772        0.886
  120     33      12    0.691  0.0450        0.608        0.785




   
## Univariable Cox Proportional Hazard Model for:  RADIATION_YN

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RADIATION_YN, data = data)

  n= 668, number of events= 178 
   (7 observations deleted due to missingness)

                   coef exp(coef) se(coef)      z Pr(>|z|)  
RADIATION_YNYes -0.4091    0.6642   0.1729 -2.367   0.0179 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                exp(coef) exp(-coef) lower .95 upper .95
RADIATION_YNYes    0.6642      1.506    0.4733    0.9321

Concordance= 0.54  (se = 0.019 )
Rsquare= 0.009   (max possible= 0.955 )
Likelihood ratio test= 5.96  on 1 df,   p=0.01462
Wald test            = 5.6  on 1 df,   p=0.01795
Score (logrank) test = 5.68  on 1 df,   p=0.01717





   
## Unadjusted Kaplan Meier Overall Survival Curve for:  RADIATION_YN

Chemo Yes/No

uni_var(test_var = "CHEMO_YN", data_imp = data)
_________________________________________________
   
## CHEMO_YN
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CHEMO_YN, data = data)

               n events median 0.95LCL 0.95UCL
CHEMO_YN=No  563    142    153   134.1      NA
CHEMO_YN=Yes  74     27    137    88.7      NA
CHEMO_YN=Ukn  38     11    128    89.8      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CHEMO_YN, data = data)

                CHEMO_YN=No 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    468      36    0.933  0.0108        0.912        0.954
   24    417      14    0.904  0.0130        0.879        0.930
   36    359      19    0.860  0.0157        0.830        0.892
   48    301      16    0.820  0.0180        0.785        0.856
   60    257      11    0.788  0.0196        0.751        0.828
  120     66      40    0.603  0.0311        0.545        0.667

                CHEMO_YN=Yes 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     64       8    0.890  0.0367        0.821        0.965
   24     52       6    0.800  0.0480        0.711        0.900
   36     45       5    0.723  0.0544        0.624        0.838
   48     38       2    0.689  0.0569        0.586        0.810
   60     36       0    0.689  0.0569        0.586        0.810
  120      9       5    0.523  0.0834        0.383        0.715

                CHEMO_YN=Ukn 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     32       2    0.944  0.0382        0.873        1.000
   24     28       3    0.855  0.0601        0.745        0.981
   36     23       1    0.819  0.0674        0.697        0.963
   48     20       1    0.782  0.0739        0.650        0.941
   60     17       2    0.697  0.0869        0.546        0.890
  120      4       1    0.634  0.0995        0.466        0.862




   
## Univariable Cox Proportional Hazard Model for:  CHEMO_YN

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CHEMO_YN, data = data)

  n= 675, number of events= 180 

              coef exp(coef) se(coef)     z Pr(>|z|)  
CHEMO_YNYes 0.3849    1.4694   0.2101 1.832    0.067 .
CHEMO_YNUkn 0.1531    1.1655   0.3132 0.489    0.625  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

            exp(coef) exp(-coef) lower .95 upper .95
CHEMO_YNYes     1.469     0.6805    0.9734     2.218
CHEMO_YNUkn     1.165     0.8580    0.6309     2.153

Concordance= 0.533  (se = 0.015 )
Rsquare= 0.005   (max possible= 0.956 )
Likelihood ratio test= 3.17  on 2 df,   p=0.2046
Wald test            = 3.44  on 2 df,   p=0.179
Score (logrank) test = 3.48  on 2 df,   p=0.1754
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  CHEMO_YN

Treatment Yes/No

uni_var(test_var = "Tx_YN", data_imp = data)
_________________________________________________
   
## Tx_YN
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ Tx_YN, data = data)

   38 observations deleted due to missingness 
              n events median 0.95LCL 0.95UCL
Tx_YN=FALSE  80     35   39.2    34.2      NA
Tx_YN=TRUE  557    134  153.1   136.8      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ Tx_YN, data = data)

38 observations deleted due to missingness 
                Tx_YN=FALSE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     48      16    0.769  0.0511        0.675        0.875
   24     37       5    0.680  0.0586        0.574        0.805
   36     25       6    0.556  0.0665        0.440        0.703
   48     16       5    0.436  0.0707        0.318        0.600
   60     15       1    0.409  0.0714        0.291        0.576
  120      1       2    0.291  0.0894        0.159        0.531

                Tx_YN=TRUE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    484      28    0.948 0.00959        0.929        0.967
   24    432      15    0.917 0.01214        0.894        0.941
   36    379      18    0.878 0.01477        0.849        0.907
   48    323      13    0.846 0.01667        0.814        0.879
   60    278      10    0.818 0.01828        0.783        0.855
  120     74      43    0.628 0.03052        0.571        0.691




   
## Univariable Cox Proportional Hazard Model for:  Tx_YN

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ Tx_YN, data = data)

  n= 637, number of events= 169 
   (38 observations deleted due to missingness)

             coef exp(coef) se(coef)      z Pr(>|z|)    
Tx_YNTRUE -1.4226    0.2411   0.1950 -7.296 2.97e-13 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

          exp(coef) exp(-coef) lower .95 upper .95
Tx_YNTRUE    0.2411      4.148    0.1645    0.3533

Concordance= 0.595  (se = 0.012 )
Rsquare= 0.062   (max possible= 0.953 )
Likelihood ratio test= 40.63  on 1 df,   p=1.841e-10
Wald test            = 53.23  on 1 df,   p=2.973e-13
Score (logrank) test = 62.55  on 1 df,   p=2.554e-15





   
## Unadjusted Kaplan Meier Overall Survival Curve for:  Tx_YN

Metastases at Dx

uni_var(test_var = "mets_at_dx_F", data_imp = data)
_________________________________________________
   
## mets_at_dx_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ mets_at_dx_F, data = data)

                     n events median 0.95LCL 0.95UCL
mets_at_dx_F=FALSE 660    170  153.1   134.1      NA
mets_at_dx_F=TRUE   15     10   21.6    12.2      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ mets_at_dx_F, data = data)

                mets_at_dx_F=FALSE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    554      42    0.933 0.00996        0.914        0.953
   24    491      20    0.898 0.01231        0.874        0.922
   36    424      22    0.856 0.01467        0.827        0.885
   48    358      19    0.815 0.01665        0.783        0.849
   60    309      13    0.784 0.01814        0.749        0.820
  120     79      46    0.605 0.02845        0.552        0.663

                mets_at_dx_F=TRUE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     10       4    0.718   0.120       0.5177        0.996
   24      6       3    0.485   0.138       0.2778        0.845
   36      3       3    0.242   0.120       0.0914        0.642
   48      1       0    0.242   0.120       0.0914        0.642
   60      1       0    0.242   0.120       0.0914        0.642




   
## Univariable Cox Proportional Hazard Model for:  mets_at_dx_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ mets_at_dx_F, data = data)

  n= 675, number of events= 180 

                  coef exp(coef) se(coef)     z Pr(>|z|)    
mets_at_dx_FTRUE 1.901     6.694    0.332 5.727 1.02e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                 exp(coef) exp(-coef) lower .95 upper .95
mets_at_dx_FTRUE     6.694     0.1494     3.492     12.83

Concordance= 0.531  (se = 0.005 )
Rsquare= 0.03   (max possible= 0.956 )
Likelihood ratio test= 20.3  on 1 df,   p=6.622e-06
Wald test            = 32.8  on 1 df,   p=1.023e-08
Score (logrank) test = 43.91  on 1 df,   p=3.435e-11





   
## Unadjusted Kaplan Meier Overall Survival Curve for:  mets_at_dx_F

Tumor Size

uni_var(test_var = "T_SIZE", data_imp = data)
_________________________________________________
   
## T_SIZE
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ T_SIZE, data = data)

                           n events median 0.95LCL 0.95UCL
T_SIZE=No Tumor            4      1     NA   12.58      NA
T_SIZE=Microscopic focus  17      3     NA   70.54      NA
T_SIZE=< 1 cm             84     21     NA  107.93      NA
T_SIZE=1-2 cm             91     16  136.8  136.77      NA
T_SIZE=2-3 cm             53     17  127.7   77.34      NA
T_SIZE=3-4 cm             27     12   83.6   46.95      NA
T_SIZE=4-5 cm             11      6   69.7    7.52      NA
T_SIZE=5-6 cm              8      4  116.2   21.65      NA
T_SIZE=>6 cm              31     14   88.7   35.81      NA
T_SIZE=NA_unk            349     86  153.1  136.97      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ T_SIZE, data = data)

                T_SIZE=No Tumor 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      3       0    1.000   0.000          1.0            1
   24      2       1    0.667   0.272          0.3            1
   36      1       0    0.667   0.272          0.3            1
   48      1       0    0.667   0.272          0.3            1
   60      1       0    0.667   0.272          0.3            1
  120      1       0    0.667   0.272          0.3            1

                T_SIZE=Microscopic focus 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     14       1    0.941  0.0571        0.836            1
   24     13       0    0.941  0.0571        0.836            1
   36     12       0    0.941  0.0571        0.836            1
   48     11       0    0.941  0.0571        0.836            1
   60      8       0    0.941  0.0571        0.836            1
  120      1       2    0.686  0.1608        0.434            1

                T_SIZE=< 1 cm 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     76       4    0.951  0.0237        0.906        0.999
   24     69       1    0.938  0.0268        0.887        0.992
   36     60       4    0.881  0.0374        0.811        0.958
   48     48       3    0.836  0.0436        0.755        0.926
   60     40       4    0.765  0.0525        0.669        0.875
  120     16       5    0.623  0.0720        0.497        0.782

                T_SIZE=1-2 cm 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     83       2    0.977  0.0161        0.946        1.000
   24     71       3    0.939  0.0263        0.889        0.992
   36     62       2    0.911  0.0321        0.851        0.977
   48     59       0    0.911  0.0321        0.851        0.977
   60     49       1    0.896  0.0351        0.830        0.968
  120     12       7    0.727  0.0659        0.608        0.868

                T_SIZE=2-3 cm 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     40       5    0.899  0.0430        0.818        0.987
   24     36       2    0.853  0.0517        0.757        0.960
   36     32       1    0.827  0.0563        0.724        0.945
   48     30       1    0.800  0.0604        0.690        0.928
   60     28       0    0.800  0.0604        0.690        0.928
  120      6       6    0.554  0.0955        0.395        0.777

                T_SIZE=3-4 cm 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     22       5    0.815  0.0748        0.681        0.975
   24     16       2    0.735  0.0864        0.583        0.925
   36     14       1    0.689  0.0924        0.529        0.896
   48     12       1    0.636  0.0994        0.468        0.864
   60     10       1    0.583  0.1042        0.410        0.827

                T_SIZE=4-5 cm 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      6       4    0.614   0.153        0.377        0.999
   24      6       0    0.614   0.153        0.377        0.999
   36      5       0    0.614   0.153        0.377        0.999
   48      5       0    0.614   0.153        0.377        0.999
   60      4       0    0.614   0.153        0.377        0.999

                T_SIZE=5-6 cm 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      7       1    0.875   0.117       0.6734            1
   24      3       1    0.656   0.209       0.3518            1
   36      3       0    0.656   0.209       0.3518            1
   48      2       0    0.656   0.209       0.3518            1
   60      2       0    0.656   0.209       0.3518            1
  120      1       1    0.328   0.254       0.0718            1

                T_SIZE=>6 cm 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     27       3    0.901  0.0543        0.801        1.000
   24     21       3    0.795  0.0748        0.661        0.956
   36     15       4    0.632  0.0942        0.472        0.846
   48      8       2    0.538  0.1010        0.372        0.777
   60      8       0    0.538  0.1010        0.372        0.777

                T_SIZE=NA_unk 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    286      21    0.936  0.0135        0.910        0.963
   24    260      10    0.902  0.0167        0.870        0.936
   36    223      13    0.856  0.0203        0.817        0.896
   48    183      12    0.807  0.0236        0.762        0.854
   60    160       7    0.773  0.0257        0.725        0.826
  120     42      19    0.627  0.0384        0.556        0.707




   
## Univariable Cox Proportional Hazard Model for:  T_SIZE

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ T_SIZE, data = data)

  n= 675, number of events= 180 

                            coef exp(coef) se(coef)      z Pr(>|z|)
T_SIZEMicroscopic focus -0.59141   0.55354  1.15574 -0.512    0.609
T_SIZE< 1 cm            -0.46595   0.62754  1.02471 -0.455    0.649
T_SIZE1-2 cm            -0.70358   0.49481  1.03152 -0.682    0.495
T_SIZE2-3 cm            -0.03174   0.96876  1.02979 -0.031    0.975
T_SIZE3-4 cm             0.61253   1.84509  1.04229  0.588    0.557
T_SIZE4-5 cm             0.98053   2.66586  1.08187  0.906    0.365
T_SIZE5-6 cm             0.63561   1.88818  1.11838  0.568    0.570
T_SIZE>6 cm              0.64804   1.91179  1.03634  0.625    0.532
T_SIZENA_unk            -0.31738   0.72806  1.00654 -0.315    0.753

                        exp(coef) exp(-coef) lower .95 upper .95
T_SIZEMicroscopic focus    0.5535     1.8065   0.05746     5.332
T_SIZE< 1 cm               0.6275     1.5935   0.08422     4.676
T_SIZE1-2 cm               0.4948     2.0210   0.06553     3.737
T_SIZE2-3 cm               0.9688     1.0322   0.12872     7.291
T_SIZE3-4 cm               1.8451     0.5420   0.23923    14.230
T_SIZE4-5 cm               2.6659     0.3751   0.31985    22.219
T_SIZE5-6 cm               1.8882     0.5296   0.21090    16.905
T_SIZE>6 cm                1.9118     0.5231   0.25079    14.574
T_SIZENA_unk               0.7281     1.3735   0.10125     5.235

Concordance= 0.6  (se = 0.022 )
Rsquare= 0.044   (max possible= 0.956 )
Likelihood ratio test= 30.07  on 9 df,   p=0.0004276
Wald test            = 35.44  on 9 df,   p=4.986e-05
Score (logrank) test = 39.19  on 9 df,   p=1.064e-05
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  T_SIZE

Tumor specific Variables

Node Size

Cox Proportional Hazard Ratio

Model #1

Full analysis

model_one %>% summary()
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_TYPE_F + FACILITY_LOCATION_F + CROWFLY + DX_STAGING_PROC_DAYS + 
    CHEMO_YN + RADIATION_YN + SURGERY_YN + IMMUNO_YN + AGE_F + 
    SEX_F + RACE_F + HISPANIC + INSURANCE_F + INCOME_F + EDUCATION_F + 
    YEAR_OF_DIAGNOSIS, data = data)

  n= 456, number of events= 129 
   (219 observations deleted due to missingness)

                                                   coef  exp(coef)   se(coef)      z Pr(>|z|)    
FACILITY_TYPE_FComprehensive Comm Ca Program  1.370e-01  1.147e+00  3.479e-01  0.394 0.693750    
FACILITY_TYPE_FAcademic/Research Program      4.250e-02  1.043e+00  3.723e-01  0.114 0.909128    
FACILITY_TYPE_FIntegrated Network Ca Program  1.010e-01  1.106e+00  3.903e-01  0.259 0.795835    
FACILITY_LOCATION_FMiddle Atlantic            1.806e+00  6.087e+00  7.103e-01  2.543 0.011003 *  
FACILITY_LOCATION_FSouth Atlantic             2.093e+00  8.106e+00  6.661e-01  3.142 0.001679 ** 
FACILITY_LOCATION_FEast North Central         2.309e+00  1.006e+01  6.626e-01  3.484 0.000494 ***
FACILITY_LOCATION_FEast South Central         2.369e+00  1.069e+01  7.032e-01  3.369 0.000754 ***
FACILITY_LOCATION_FWest North Central         2.274e+00  9.715e+00  6.951e-01  3.271 0.001071 ** 
FACILITY_LOCATION_FWest South Central         2.978e+00  1.966e+01  6.893e-01  4.321 1.55e-05 ***
FACILITY_LOCATION_FMountain                   2.908e+00  1.832e+01  8.031e-01  3.621 0.000293 ***
FACILITY_LOCATION_FPacific                    3.059e+00  2.130e+01  8.100e-01  3.776 0.000159 ***
CROWFLY                                      -1.014e-03  9.990e-01  1.517e-03 -0.668 0.503954    
DX_STAGING_PROC_DAYS                         -5.706e-03  9.943e-01  1.145e-02 -0.498 0.618404    
CHEMO_YNYes                                   1.815e+00  6.139e+00  3.409e-01  5.322 1.02e-07 ***
CHEMO_YNUkn                                   1.744e-01  1.190e+00  5.398e-01  0.323 0.746700    
RADIATION_YNYes                              -2.055e-01  8.143e-01  2.411e-01 -0.852 0.394097    
SURGERY_YNUkn                                 8.573e-01  2.357e+00  8.045e-01  1.066 0.286601    
SURGERY_YNYes                                -2.134e+00  1.184e-01  2.514e-01 -8.489  < 2e-16 ***
IMMUNO_YNYes                                 -1.580e+01  1.377e-07  2.476e+03 -0.006 0.994910    
IMMUNO_YNUkn                                  5.508e-01  1.735e+00  1.190e+00  0.463 0.643396    
AGE_F(54,64]                                  2.704e-01  1.311e+00  4.189e-01  0.646 0.518556    
AGE_F(64,74]                                  1.100e+00  3.005e+00  4.532e-01  2.428 0.015180 *  
AGE_F(74,100]                                 1.891e+00  6.623e+00  4.748e-01  3.982 6.84e-05 ***
SEX_FFemale                                   4.116e-01  1.509e+00  8.133e-01  0.506 0.612768    
RACE_FBlack                                  -7.280e-02  9.298e-01  3.458e-01 -0.211 0.833265    
RACE_FOther/Unk                              -8.538e-01  4.258e-01  8.992e-01 -0.950 0.342354    
RACE_FAsian                                  -1.591e+00  2.038e-01  1.163e+00 -1.368 0.171263    
HISPANICYes                                  -1.271e+00  2.807e-01  7.848e-01 -1.619 0.105431    
HISPANICUnknown                              -7.433e-01  4.755e-01  3.884e-01 -1.914 0.055658 .  
INSURANCE_FNone                               2.439e+00  1.146e+01  5.940e-01  4.106 4.03e-05 ***
INSURANCE_FMedicaid                           8.471e-01  2.333e+00  6.489e-01  1.305 0.191756    
INSURANCE_FMedicare                           7.426e-01  2.101e+00  3.441e-01  2.158 0.030895 *  
INSURANCE_FOther Government                   8.858e-01  2.425e+00  1.198e+00  0.740 0.459488    
INSURANCE_FUnknown                            2.792e-01  1.322e+00  6.895e-01  0.405 0.685581    
INCOME_F$38,000 - $47,999                    -9.580e-02  9.086e-01  3.518e-01 -0.272 0.785414    
INCOME_F$48,000 - $62,999                    -4.004e-01  6.700e-01  3.522e-01 -1.137 0.255613    
INCOME_F$63,000 +                             5.546e-02  1.057e+00  4.313e-01  0.129 0.897686    
EDUCATION_F13 - 20.9%                         4.014e-01  1.494e+00  3.532e-01  1.136 0.255830    
EDUCATION_F7 - 12.9%                          2.774e-01  1.320e+00  4.161e-01  0.667 0.504966    
EDUCATION_FLess than 7%                      -2.669e-01  7.657e-01  4.933e-01 -0.541 0.588382    
YEAR_OF_DIAGNOSIS2005                        -3.466e-01  7.071e-01  5.298e-01 -0.654 0.513016    
YEAR_OF_DIAGNOSIS2006                         3.065e-01  1.359e+00  4.297e-01  0.713 0.475762    
YEAR_OF_DIAGNOSIS2007                         3.427e-01  1.409e+00  4.611e-01  0.743 0.457298    
YEAR_OF_DIAGNOSIS2008                         5.513e-01  1.736e+00  5.295e-01  1.041 0.297769    
YEAR_OF_DIAGNOSIS2009                        -2.187e-01  8.036e-01  5.293e-01 -0.413 0.679515    
YEAR_OF_DIAGNOSIS2010                        -5.171e-01  5.963e-01  5.169e-01 -1.000 0.317112    
YEAR_OF_DIAGNOSIS2011                        -2.753e-01  7.593e-01  5.037e-01 -0.547 0.584621    
YEAR_OF_DIAGNOSIS2012                        -1.114e+00  3.282e-01  6.149e-01 -1.812 0.070018 .  
YEAR_OF_DIAGNOSIS2013                         2.602e-01  1.297e+00  5.368e-01  0.485 0.627877    
YEAR_OF_DIAGNOSIS2014                        -5.479e-01  5.782e-01  6.566e-01 -0.834 0.404054    
YEAR_OF_DIAGNOSIS2015                        -9.574e-02  9.087e-01  6.250e-01 -0.153 0.878264    
YEAR_OF_DIAGNOSIS2016                                NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                             exp(coef) exp(-coef) lower .95 upper .95
FACILITY_TYPE_FComprehensive Comm Ca Program 1.147e+00  8.720e-01   0.57991    2.2679
FACILITY_TYPE_FAcademic/Research Program     1.043e+00  9.584e-01   0.50297    2.1646
FACILITY_TYPE_FIntegrated Network Ca Program 1.106e+00  9.039e-01   0.51481    2.3772
FACILITY_LOCATION_FMiddle Atlantic           6.087e+00  1.643e-01   1.51265   24.4918
FACILITY_LOCATION_FSouth Atlantic            8.106e+00  1.234e-01   2.19715   29.9057
FACILITY_LOCATION_FEast North Central        1.006e+01  9.938e-02   2.74560   36.8750
FACILITY_LOCATION_FEast South Central        1.069e+01  9.356e-02   2.69361   42.4118
FACILITY_LOCATION_FWest North Central        9.715e+00  1.029e-01   2.48774   37.9420
FACILITY_LOCATION_FWest South Central        1.966e+01  5.087e-02   5.09128   75.8959
FACILITY_LOCATION_FMountain                  1.832e+01  5.458e-02   3.79634   88.4394
FACILITY_LOCATION_FPacific                   2.130e+01  4.696e-02   4.35371  104.1707
CROWFLY                                      9.990e-01  1.001e+00   0.99602    1.0020
DX_STAGING_PROC_DAYS                         9.943e-01  1.006e+00   0.97224    1.0169
CHEMO_YNYes                                  6.139e+00  1.629e-01   3.14693   11.9759
CHEMO_YNUkn                                  1.190e+00  8.400e-01   0.41325    3.4296
RADIATION_YNYes                              8.143e-01  1.228e+00   0.50764    1.3061
SURGERY_YNUkn                                2.357e+00  4.243e-01   0.48698   11.4062
SURGERY_YNYes                                1.184e-01  8.448e+00   0.07232    0.1937
IMMUNO_YNYes                                 1.377e-07  7.263e+06   0.00000       Inf
IMMUNO_YNUkn                                 1.735e+00  5.765e-01   0.16845   17.8638
AGE_F(54,64]                                 1.311e+00  7.630e-01   0.57660    2.9787
AGE_F(64,74]                                 3.005e+00  3.328e-01   1.23630    7.3046
AGE_F(74,100]                                6.623e+00  1.510e-01   2.61157   16.7961
SEX_FFemale                                  1.509e+00  6.626e-01   0.30653    7.4316
RACE_FBlack                                  9.298e-01  1.076e+00   0.47208    1.8312
RACE_FOther/Unk                              4.258e-01  2.349e+00   0.07308    2.4808
RACE_FAsian                                  2.038e-01  4.907e+00   0.02087    1.9899
HISPANICYes                                  2.807e-01  3.563e+00   0.06028    1.3067
HISPANICUnknown                              4.755e-01  2.103e+00   0.22210    1.0181
INSURANCE_FNone                              1.146e+01  8.726e-02   3.57741   36.7136
INSURANCE_FMedicaid                          2.333e+00  4.286e-01   0.65392    8.3230
INSURANCE_FMedicare                          2.101e+00  4.759e-01   1.07067    4.1245
INSURANCE_FOther Government                  2.425e+00  4.124e-01   0.23193   25.3536
INSURANCE_FUnknown                           1.322e+00  7.564e-01   0.34222    5.1071
INCOME_F$38,000 - $47,999                    9.086e-01  1.101e+00   0.45594    1.8109
INCOME_F$48,000 - $62,999                    6.700e-01  1.492e+00   0.33596    1.3363
INCOME_F$63,000 +                            1.057e+00  9.460e-01   0.45389    2.4617
EDUCATION_F13 - 20.9%                        1.494e+00  6.694e-01   0.74755    2.9854
EDUCATION_F7 - 12.9%                         1.320e+00  7.577e-01   0.58381    2.9834
EDUCATION_FLess than 7%                      7.657e-01  1.306e+00   0.29120    2.0134
YEAR_OF_DIAGNOSIS2005                        7.071e-01  1.414e+00   0.25033    1.9974
YEAR_OF_DIAGNOSIS2006                        1.359e+00  7.360e-01   0.58519    3.1542
YEAR_OF_DIAGNOSIS2007                        1.409e+00  7.098e-01   0.57064    3.4780
YEAR_OF_DIAGNOSIS2008                        1.736e+00  5.762e-01   0.61480    4.8993
YEAR_OF_DIAGNOSIS2009                        8.036e-01  1.244e+00   0.28480    2.2675
YEAR_OF_DIAGNOSIS2010                        5.963e-01  1.677e+00   0.21651    1.6421
YEAR_OF_DIAGNOSIS2011                        7.593e-01  1.317e+00   0.28293    2.0378
YEAR_OF_DIAGNOSIS2012                        3.282e-01  3.047e+00   0.09835    1.0954
YEAR_OF_DIAGNOSIS2013                        1.297e+00  7.709e-01   0.45300    3.7145
YEAR_OF_DIAGNOSIS2014                        5.782e-01  1.730e+00   0.15964    2.0940
YEAR_OF_DIAGNOSIS2015                        9.087e-01  1.100e+00   0.26693    3.0935
YEAR_OF_DIAGNOSIS2016                               NA         NA        NA        NA

Concordance= 0.837  (se = 0.028 )
Rsquare= 0.36   (max possible= 0.956 )
Likelihood ratio test= 203.2  on 51 df,   p=0
Wald test            = 168.7  on 51 df,   p=1.654e-14
Score (logrank) test = 225.4  on 51 df,   p=0

Summary of Model

Linear Regression

#only include rows with known treatment information, n = 82
data2 <- data %>% filter(SURGERY_YN != "Ukn" & RADIATION_YN != "Ukn"
                         & CHEMO_YN != "Ukn" & IMMUNO_YN != "Ukn")
# include only variables with data available for at least 75% cases 
# from the following set of variables of interest:
## FACILITY_TYPE_F + FACILITY_GEOGRAPHY + CROWFLY + 
##                 DX_STAGING_PROC_DAYS + 
##                 CHEMO_YN + RADIATION_YN + SURGERY_YN + IMMUNO_YN +
##                 AGE + SEX_F + RACE_F + HISPANIC + INSURANCE_F + INCOME_F + 
##                 EDUCATION_F + YEAR_OF_DIAGNOSIS + SITE_TEXT + GRADE_F
length(which(is.na(data2$SITE_TEXT))) / nrow(data2)
[1] 0.3349282
# excluded the following in this analysis due to missing data: 
#  DX_STAGING_PROC_DAYS, GRADE_F (mostly unknowns), SITE_TEXT
fit_surv <- lm(DX_LASTCONTACT_DEATH_MONTHS ~
                 FACILITY_TYPE_F + 
                 CHEMO_YN + SURGERY_YN +
                 AGE_F + INSURANCE_F + INCOME_F + 
                 EDUCATION_F + YEAR_OF_DIAGNOSIS,
   data = data2)
summary(fit_surv) # R^2 = 0.4207, p < 2.2e-16

Call:
lm(formula = DX_LASTCONTACT_DEATH_MONTHS ~ FACILITY_TYPE_F + 
    CHEMO_YN + SURGERY_YN + AGE_F + INSURANCE_F + INCOME_F + 
    EDUCATION_F + YEAR_OF_DIAGNOSIS, data = data2)

Residuals:
    Min      1Q  Median      3Q     Max 
-92.604 -18.865   2.862  22.396  86.519 

Coefficients:
                                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                    80.398      8.531   9.424  < 2e-16 ***
FACILITY_TYPE_FComprehensive Comm Ca Program   -9.538      4.294  -2.221 0.026732 *  
FACILITY_TYPE_FAcademic/Research Program       -8.646      4.585  -1.886 0.059832 .  
FACILITY_TYPE_FIntegrated Network Ca Program   -6.480      5.251  -1.234 0.217709    
CHEMO_YNYes                                   -13.503      4.794  -2.817 0.005019 ** 
SURGERY_YNYes                                  27.347      3.706   7.380 5.73e-13 ***
AGE_F(54,64]                                   -9.364      4.198  -2.231 0.026102 *  
AGE_F(64,74]                                   -6.979      4.981  -1.401 0.161698    
AGE_F(74,100]                                 -20.884      5.144  -4.060 5.61e-05 ***
INSURANCE_FNone                               -16.287      8.152  -1.998 0.046207 *  
INSURANCE_FMedicaid                             1.686      6.533   0.258 0.796435    
INSURANCE_FMedicare                            -3.798      4.120  -0.922 0.356913    
INSURANCE_FOther Government                    -8.467     11.939  -0.709 0.478529    
INSURANCE_FUnknown                             13.544     10.481   1.292 0.196774    
INCOME_F$38,000 - $47,999                      -4.625      4.576  -1.011 0.312593    
INCOME_F$48,000 - $62,999                      -5.857      4.869  -1.203 0.229518    
INCOME_F$63,000 +                             -10.180      5.423  -1.877 0.060982 .  
EDUCATION_F13 - 20.9%                          -3.511      4.802  -0.731 0.464950    
EDUCATION_F7 - 12.9%                           10.095      5.161   1.956 0.050932 .  
EDUCATION_FLess than 7%                        15.440      5.991   2.577 0.010219 *  
YEAR_OF_DIAGNOSIS2005                          10.294      7.055   1.459 0.145087    
YEAR_OF_DIAGNOSIS2006                          -1.357      6.616  -0.205 0.837608    
YEAR_OF_DIAGNOSIS2007                          -3.403      6.804  -0.500 0.617179    
YEAR_OF_DIAGNOSIS2008                          -4.147      7.758  -0.535 0.593168    
YEAR_OF_DIAGNOSIS2009                         -18.090      6.879  -2.630 0.008783 ** 
YEAR_OF_DIAGNOSIS2010                         -21.321      7.203  -2.960 0.003207 ** 
YEAR_OF_DIAGNOSIS2011                         -26.654      6.912  -3.856 0.000128 ***
YEAR_OF_DIAGNOSIS2012                         -36.324      7.120  -5.102 4.61e-07 ***
YEAR_OF_DIAGNOSIS2013                         -52.461      6.898  -7.605 1.21e-13 ***
YEAR_OF_DIAGNOSIS2014                         -57.082      7.358  -7.758 4.10e-14 ***
YEAR_OF_DIAGNOSIS2015                         -62.495      6.939  -9.006  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 32.54 on 562 degrees of freedom
  (34 observations deleted due to missingness)
Multiple R-squared:  0.4501,    Adjusted R-squared:  0.4207 
F-statistic: 15.33 on 30 and 562 DF,  p-value: < 2.2e-16
# the following variables were excluded to 
# improve the R-squared of the regression (initially R^2 = 0.4106):
#  HISPANIC, RACE_F, FACILITY_LOCATION_F, CROWFLY, RADIATION_YN, IMMUNO_YN, SEX_F

Prediction Logistic Regression Models

Surgery

no_Ukns <- data2 %>%
  droplevels() %>% 
  mutate(SURGERY_YN = as.logical(SURGERY_YN))
# excluded the following in this analysis due to missing data: 
#  DX_STAGING_PROC_DAYS, GRADE_F (mostly unknowns) + SITE_TEXT
fit_surg <- glm(SURG_TF ~ 
                 FACILITY_TYPE_F + FACILITY_LOCATION_F + 
                 CHEMO_YN + RADIATION_YN + IMMUNO_YN +
                 AGE_F + SEX_F + RACE_F + HISPANIC + INSURANCE_F + INCOME_F + 
                 EDUCATION_F + YEAR_OF_DIAGNOSIS,
   data = no_Ukns)
summary(fit_surg)

Call:
glm(formula = SURG_TF ~ FACILITY_TYPE_F + FACILITY_LOCATION_F + 
    CHEMO_YN + RADIATION_YN + IMMUNO_YN + AGE_F + SEX_F + RACE_F + 
    HISPANIC + INSURANCE_F + INCOME_F + EDUCATION_F + YEAR_OF_DIAGNOSIS, 
    data = no_Ukns)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-1.00845  -0.05292   0.10994   0.21927   0.55376  

Coefficients:
                                              Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                   0.651660   0.145713   4.472 9.42e-06 ***
FACILITY_TYPE_FComprehensive Comm Ca Program  0.022366   0.049984   0.447 0.654716    
FACILITY_TYPE_FAcademic/Research Program      0.005020   0.053528   0.094 0.925311    
FACILITY_TYPE_FIntegrated Network Ca Program  0.042396   0.060626   0.699 0.484654    
FACILITY_LOCATION_FMiddle Atlantic            0.121849   0.082802   1.472 0.141710    
FACILITY_LOCATION_FSouth Atlantic             0.236085   0.078723   2.999 0.002832 ** 
FACILITY_LOCATION_FEast North Central         0.233113   0.079585   2.929 0.003541 ** 
FACILITY_LOCATION_FEast South Central         0.246452   0.094099   2.619 0.009061 ** 
FACILITY_LOCATION_FWest North Central         0.226257   0.089148   2.538 0.011426 *  
FACILITY_LOCATION_FWest South Central         0.160957   0.088551   1.818 0.069661 .  
FACILITY_LOCATION_FMountain                   0.246836   0.099376   2.484 0.013295 *  
FACILITY_LOCATION_FPacific                    0.359396   0.095469   3.765 0.000185 ***
CHEMO_YNYes                                  -0.167294   0.056832  -2.944 0.003381 ** 
RADIATION_YNYes                               0.084956   0.034938   2.432 0.015351 *  
IMMUNO_YNYes                                  0.486867   0.180276   2.701 0.007134 ** 
AGE_F(54,64]                                 -0.083242   0.047053  -1.769 0.077436 .  
AGE_F(64,74]                                  0.010667   0.056472   0.189 0.850243    
AGE_F(74,100]                                -0.125091   0.058317  -2.145 0.032390 *  
SEX_FFemale                                  -0.007287   0.095848  -0.076 0.939424    
RACE_FBlack                                  -0.105561   0.053772  -1.963 0.050137 .  
RACE_FOther/Unk                              -0.145030   0.114562  -1.266 0.206070    
RACE_FAsian                                  -0.052305   0.117234  -0.446 0.655660    
HISPANICYes                                  -0.075258   0.091294  -0.824 0.410103    
HISPANICUnknown                               0.039973   0.056558   0.707 0.480018    
INSURANCE_FNone                              -0.265593   0.093010  -2.856 0.004459 ** 
INSURANCE_FMedicaid                          -0.103641   0.076063  -1.363 0.173579    
INSURANCE_FMedicare                          -0.066688   0.046605  -1.431 0.153020    
INSURANCE_FOther Government                   0.074204   0.135694   0.547 0.584708    
INSURANCE_FUnknown                           -0.275175   0.118496  -2.322 0.020587 *  
INCOME_F$38,000 - $47,999                     0.012457   0.051984   0.240 0.810701    
INCOME_F$48,000 - $62,999                    -0.025082   0.055799  -0.450 0.653240    
INCOME_F$63,000 +                             0.021385   0.062719   0.341 0.733257    
EDUCATION_F13 - 20.9%                         0.034352   0.055219   0.622 0.534138    
EDUCATION_F7 - 12.9%                          0.073091   0.060256   1.213 0.225649    
EDUCATION_FLess than 7%                       0.048306   0.070943   0.681 0.496217    
YEAR_OF_DIAGNOSIS2005                         0.091067   0.080623   1.130 0.259160    
YEAR_OF_DIAGNOSIS2006                         0.111026   0.075344   1.474 0.141171    
YEAR_OF_DIAGNOSIS2007                         0.010534   0.076977   0.137 0.891205    
YEAR_OF_DIAGNOSIS2008                         0.030122   0.087763   0.343 0.731563    
YEAR_OF_DIAGNOSIS2009                        -0.026367   0.078985  -0.334 0.738642    
YEAR_OF_DIAGNOSIS2010                        -0.033775   0.082049  -0.412 0.680762    
YEAR_OF_DIAGNOSIS2011                         0.036652   0.078686   0.466 0.641544    
YEAR_OF_DIAGNOSIS2012                         0.022172   0.081462   0.272 0.785588    
YEAR_OF_DIAGNOSIS2013                        -0.007665   0.078903  -0.097 0.922648    
YEAR_OF_DIAGNOSIS2014                        -0.126214   0.084491  -1.494 0.135798    
YEAR_OF_DIAGNOSIS2015                         0.001531   0.078824   0.019 0.984506    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.1320378)

    Null deviance: 85.761  on 592  degrees of freedom
Residual deviance: 72.225  on 547  degrees of freedom
  (34 observations deleted due to missingness)
AIC: 528.35

Number of Fisher Scoring iterations: 2
# the following variables were excluded to 
# improve the R-squared of the regression (initially residual = 72.225):
# none
exp(cbind("Odds ratio" = coef(fit_surg), confint.default(fit_surg, level = 0.95)))
                                             Odds ratio     2.5 %    97.5 %
(Intercept)                                   1.9187228 1.4420519 2.5529575
FACILITY_TYPE_FComprehensive Comm Ca Program  1.0226181 0.9271862 1.1278725
FACILITY_TYPE_FAcademic/Research Program      1.0050330 0.9049345 1.1162036
FACILITY_TYPE_FIntegrated Network Ca Program  1.0433079 0.9264196 1.1749441
FACILITY_LOCATION_FMiddle Atlantic            1.1295840 0.9603676 1.3286163
FACILITY_LOCATION_FSouth Atlantic             1.2662821 1.0852297 1.4775401
FACILITY_LOCATION_FEast North Central         1.2625237 1.0801816 1.4756464
FACILITY_LOCATION_FEast South Central         1.2794778 1.0639858 1.5386141
FACILITY_LOCATION_FWest North Central         1.2538977 1.0528798 1.4932943
FACILITY_LOCATION_FWest South Central         1.1746348 0.9874791 1.3972619
FACILITY_LOCATION_FMountain                   1.2799691 1.0534409 1.5552090
FACILITY_LOCATION_FPacific                    1.4324644 1.1880112 1.7272180
CHEMO_YNYes                                   0.8459507 0.7567800 0.9456283
RADIATION_YNYes                               1.0886690 1.0166155 1.1658294
IMMUNO_YNYes                                  1.6272099 1.1428588 2.3168323
AGE_F(54,64]                                  0.9201288 0.8390674 1.0090215
AGE_F(64,74]                                  1.0107245 0.9048236 1.1290201
AGE_F(74,100]                                 0.8824164 0.7871082 0.9892652
SEX_FFemale                                   0.9927393 0.8227151 1.1979010
RACE_FBlack                                   0.8998193 0.8098129 0.9998295
RACE_FOther/Unk                               0.8649960 0.6910324 1.0827540
RACE_FAsian                                   0.9490395 0.7542141 1.1941914
HISPANICYes                                   0.9275041 0.7755429 1.1092407
HISPANICUnknown                               1.0407826 0.9315745 1.1627932
INSURANCE_FNone                               0.7667512 0.6389760 0.9200775
INSURANCE_FMedicaid                           0.9015489 0.7766840 1.0464878
INSURANCE_FMedicare                           0.9354871 0.8538232 1.0249617
INSURANCE_FOther Government                   1.0770260 0.8255118 1.4051707
INSURANCE_FUnknown                            0.7594389 0.6020447 0.9579812
INCOME_F$38,000 - $47,999                     1.0125354 0.9144522 1.1211388
INCOME_F$48,000 - $62,999                     0.9752298 0.8742007 1.0879346
INCOME_F$63,000 +                             1.0216155 0.9034441 1.1552437
EDUCATION_F13 - 20.9%                         1.0349484 0.9287870 1.1532442
EDUCATION_F7 - 12.9%                          1.0758287 0.9559894 1.2106906
EDUCATION_FLess than 7%                       1.0494917 0.9132550 1.2060519
YEAR_OF_DIAGNOSIS2005                         1.0953429 0.9352419 1.2828510
YEAR_OF_DIAGNOSIS2006                         1.1174237 0.9640167 1.2952428
YEAR_OF_DIAGNOSIS2007                         1.0105894 0.8690647 1.1751611
YEAR_OF_DIAGNOSIS2008                         1.0305805 0.8677169 1.2240122
YEAR_OF_DIAGNOSIS2009                         0.9739776 0.8342892 1.1370546
YEAR_OF_DIAGNOSIS2010                         0.9667889 0.8231729 1.1354612
YEAR_OF_DIAGNOSIS2011                         1.0373321 0.8890779 1.2103079
YEAR_OF_DIAGNOSIS2012                         1.0224197 0.8715428 1.1994157
YEAR_OF_DIAGNOSIS2013                         0.9923644 0.8501758 1.1583334
YEAR_OF_DIAGNOSIS2014                         0.8814260 0.7469082 1.0401704
YEAR_OF_DIAGNOSIS2015                         1.0015326 0.8581640 1.1688530

Metastasis at Time of Diagnosis

fit_mets <- glm(mets_at_dx_F ~ 
                 FACILITY_TYPE_F + FACILITY_LOCATION_F + 
                 AGE_F + SEX_F + RACE_F + INSURANCE_F + INCOME_F + 
                 EDUCATION_F + YEAR_OF_DIAGNOSIS,
   data = data)
# the following variables were excluded to 
# improve the R-squared of the regression (initial residual = 12.525):
# HISPANIC + 
summary(fit_mets)

Call:
glm(formula = mets_at_dx_F ~ FACILITY_TYPE_F + FACILITY_LOCATION_F + 
    AGE_F + SEX_F + RACE_F + INSURANCE_F + INCOME_F + EDUCATION_F + 
    YEAR_OF_DIAGNOSIS, data = data)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-0.18895  -0.04985  -0.01444   0.01144   0.96172  

Coefficients:
                                              Estimate Std. Error t value Pr(>|t|)   
(Intercept)                                  -0.060588   0.057592  -1.052  0.29321   
FACILITY_TYPE_FComprehensive Comm Ca Program -0.010098   0.019712  -0.512  0.60864   
FACILITY_TYPE_FAcademic/Research Program      0.016207   0.021212   0.764  0.44516   
FACILITY_TYPE_FIntegrated Network Ca Program -0.002913   0.023971  -0.122  0.90333   
FACILITY_LOCATION_FMiddle Atlantic            0.036927   0.032567   1.134  0.25729   
FACILITY_LOCATION_FSouth Atlantic             0.026926   0.031237   0.862  0.38905   
FACILITY_LOCATION_FEast North Central         0.052368   0.031665   1.654  0.09869 . 
FACILITY_LOCATION_FEast South Central         0.008855   0.037615   0.235  0.81397   
FACILITY_LOCATION_FWest North Central         0.016227   0.035243   0.460  0.64537   
FACILITY_LOCATION_FWest South Central         0.025065   0.034905   0.718  0.47299   
FACILITY_LOCATION_FMountain                   0.038322   0.039111   0.980  0.32757   
FACILITY_LOCATION_FPacific                    0.016309   0.037563   0.434  0.66432   
AGE_F(54,64]                                 -0.044541   0.018646  -2.389  0.01722 * 
AGE_F(64,74]                                 -0.042685   0.022147  -1.927  0.05441 . 
AGE_F(74,100]                                -0.047581   0.022299  -2.134  0.03327 * 
SEX_FFemale                                   0.027386   0.038086   0.719  0.47239   
RACE_FBlack                                  -0.003492   0.020659  -0.169  0.86584   
RACE_FOther/Unk                              -0.016818   0.044897  -0.375  0.70810   
RACE_FAsian                                   0.053944   0.047789   1.129  0.25944   
INSURANCE_FNone                               0.113028   0.037154   3.042  0.00245 **
INSURANCE_FMedicaid                           0.084171   0.029490   2.854  0.00446 **
INSURANCE_FMedicare                           0.033215   0.018440   1.801  0.07217 . 
INSURANCE_FOther Government                   0.014923   0.055474   0.269  0.78801   
INSURANCE_FUnknown                            0.075632   0.044625   1.695  0.09063 . 
INCOME_F$38,000 - $47,999                     0.018432   0.020708   0.890  0.37378   
INCOME_F$48,000 - $62,999                     0.033849   0.022261   1.521  0.12891   
INCOME_F$63,000 +                             0.045191   0.024936   1.812  0.07045 . 
EDUCATION_F13 - 20.9%                        -0.009875   0.021661  -0.456  0.64865   
EDUCATION_F7 - 12.9%                         -0.024820   0.023738  -1.046  0.29619   
EDUCATION_FLess than 7%                      -0.021722   0.027644  -0.786  0.43230   
YEAR_OF_DIAGNOSIS2005                         0.003146   0.031296   0.101  0.91996   
YEAR_OF_DIAGNOSIS2006                         0.006157   0.029339   0.210  0.83386   
YEAR_OF_DIAGNOSIS2007                         0.015649   0.030588   0.512  0.60912   
YEAR_OF_DIAGNOSIS2008                        -0.001463   0.033809  -0.043  0.96550   
YEAR_OF_DIAGNOSIS2009                         0.002255   0.031048   0.073  0.94211   
YEAR_OF_DIAGNOSIS2010                         0.082894   0.031715   2.614  0.00918 **
YEAR_OF_DIAGNOSIS2011                         0.032651   0.031033   1.052  0.29316   
YEAR_OF_DIAGNOSIS2012                         0.019965   0.032511   0.614  0.53939   
YEAR_OF_DIAGNOSIS2013                         0.073395   0.030823   2.381  0.01757 * 
YEAR_OF_DIAGNOSIS2014                         0.050970   0.033018   1.544  0.12319   
YEAR_OF_DIAGNOSIS2015                         0.044258   0.031312   1.413  0.15804   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.02228879)

    Null deviance: 14.646  on 635  degrees of freedom
Residual deviance: 13.262  on 595  degrees of freedom
  (39 observations deleted due to missingness)
AIC: -572.63

Number of Fisher Scoring iterations: 2
exp(cbind("Odds ratio" = coef(fit_mets), confint.default(fit_mets, level = 0.95)))
                                             Odds ratio     2.5 %    97.5 %
(Intercept)                                   0.9412105 0.8407448 1.0536814
FACILITY_TYPE_FComprehensive Comm Ca Program  0.9899525 0.9524354 1.0289475
FACILITY_TYPE_FAcademic/Research Program      1.0163388 0.9749501 1.0594845
FACILITY_TYPE_FIntegrated Network Ca Program  0.9970915 0.9513286 1.0450558
FACILITY_LOCATION_FMiddle Atlantic            1.0376176 0.9734567 1.1060073
FACILITY_LOCATION_FSouth Atlantic             1.0272913 0.9662840 1.0921503
FACILITY_LOCATION_FEast North Central         1.0537638 0.9903539 1.1212338
FACILITY_LOCATION_FEast South Central         1.0088944 0.9371900 1.0860848
FACILITY_LOCATION_FWest North Central         1.0163597 0.9485248 1.0890459
FACILITY_LOCATION_FWest South Central         1.0253814 0.9575774 1.0979866
FACILITY_LOCATION_FMountain                   1.0390656 0.9623918 1.1218481
FACILITY_LOCATION_FPacific                    1.0164430 0.9442977 1.0941003
AGE_F(54,64]                                  0.9564369 0.9221139 0.9920374
AGE_F(64,74]                                  0.9582128 0.9175084 1.0007230
AGE_F(74,100]                                 0.9535336 0.9127561 0.9961327
SEX_FFemale                                   1.0277644 0.9538375 1.1074210
RACE_FBlack                                   0.9965144 0.9569715 1.0376912
RACE_FOther/Unk                               0.9833231 0.9004927 1.0737725
RACE_FAsian                                   1.0554253 0.9610571 1.1590596
INSURANCE_FNone                               1.1196631 1.0410271 1.2042391
INSURANCE_FMedicaid                           1.0878153 1.0267224 1.1525434
INSURANCE_FMedicare                           1.0337732 0.9970773 1.0718197
INSURANCE_FOther Government                   1.0150352 0.9104613 1.1316202
INSURANCE_FUnknown                            1.0785659 0.9882384 1.1771495
INCOME_F$38,000 - $47,999                     1.0186026 0.9780892 1.0607940
INCOME_F$48,000 - $62,999                     1.0344282 0.9902651 1.0805610
INCOME_F$63,000 +                             1.0462276 0.9963239 1.0986309
EDUCATION_F13 - 20.9%                         0.9901741 0.9490168 1.0331163
EDUCATION_F7 - 12.9%                          0.9754859 0.9311406 1.0219432
EDUCATION_FLess than 7%                       0.9785120 0.9269061 1.0329911
YEAR_OF_DIAGNOSIS2005                         1.0031509 0.9434671 1.0666103
YEAR_OF_DIAGNOSIS2006                         1.0061756 0.9499500 1.0657290
YEAR_OF_DIAGNOSIS2007                         1.0157718 0.9566641 1.0785315
YEAR_OF_DIAGNOSIS2008                         0.9985383 0.9345160 1.0669467
YEAR_OF_DIAGNOSIS2009                         1.0022579 0.9430870 1.0651413
YEAR_OF_DIAGNOSIS2010                         1.0864268 1.0209510 1.1561018
YEAR_OF_DIAGNOSIS2011                         1.0331900 0.9722211 1.0979823
YEAR_OF_DIAGNOSIS2012                         1.0201654 0.9571879 1.0872865
YEAR_OF_DIAGNOSIS2013                         1.0761555 1.0130669 1.1431730
YEAR_OF_DIAGNOSIS2014                         1.0522913 0.9863506 1.1226404
YEAR_OF_DIAGNOSIS2015                         1.0452525 0.9830345 1.1114083
---
title: "Mammary Paget Disease - Review of the NCDB"
author: "Ramie Fathy"
date: "11/05/2019"
output:
  html_notebook:
    theme: united
    toc: yes
    toc_float: yes
  html_document:
    toc: yes
---



```{r, echo=FALSE, warning=FALSE, message=FALSE}

library("ggplot2")
library("dplyr")
library("tidyr")
library("knitr")
library("tableone")
library("forcats")
library("survival")
library("npsurv")
library("broom")
library("tibble")
library("readr")
library("survminer")
library("stringr")


knitr::opts_chunk$set(echo=TRUE, warning=FALSE, message=TRUE)
'%!in%' <- function(x,y)!('%in%'(x,y))
```

```{r}
p_table <- function(tab_data, ...) {
  tab_data_2 <- deparse(substitute(tab_data))
  
  table_p <- do.call(CreateTableOne, 
                     list(data = as.name(tab_data_2), includeNA = TRUE, ...))
  table_p_out <- print(table_p,
                       showAllLevels = TRUE,
                       printToggle = FALSE)
  kable(table_p_out,
        align = "c")
}
```

```{r}
uni_var <- function(test_var, data_imp) {

                
        cat("_________________________________________________")
        cat("\n")
        cat("   \n##", test_var)
        cat("\n")
        cat("_________________________________________________")
        cat("\n")

        
        f <- as.formula(paste("Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 0)",
                              as.name(test_var),
                              sep = " ~ " ))
        
        data_imp_2 <- deparse(substitute(data_imp))

        km_fit <- do.call("survfit", list(formula = f, data = as.name(data_imp_2)))

        print(km_fit)
        cat("\n")

        print(summary(km_fit, times = c(12, 24, 36, 48, 60, 120)))
        cat("\n")


        cat("\n")
        cat("\n")
        cat("   \n## Univariable Cox Proportional Hazard Model for: ", test_var)
        cat("\n")
        cat("\n")


        n_levels <- nlevels(data_imp[[test_var]])

        if(n_levels == 1){
                print("Only one level, no Cox model performed")
                cat("\n")

        } else {


                cox_fit <- do.call("coxph", list(formula = f, data = as.name(data_imp_2)))

                print(summary(cox_fit))
                cat("\n")
                
                do.call("ggforest",
                         list(model = cox_fit, data = as.name(data_imp_2)))


        }

        cat("\n")
        cat("\n")
        cat("\n")
        cat("   \n## Unadjusted Kaplan Meier Overall Survival Curve for: ", test_var)


        p <- do.call("ggsurvplot",
                     list(fit = km_fit, data = as.name(data_imp_2),
                          palette = "jco", censor = FALSE, legend = "right",
                          linetype = "strata", xlab = "Time (Months)"))

        print(p)

}

```

```{r}
f_plot <- function(test_var, data_imp){

                
        cat("_________________________________________________")
        cat("\n")
        cat("   \n##", test_var)
        cat("\n")
        cat("_________________________________________________")
        cat("\n")

        
        f <- as.formula(paste(as.name(test_var),
                              "AGE + SEX + T_SIZE + FACILITY_TYPE_F + FACILITY_LOCATION_F + YEAR_OF_DIAGNOSIS",
                              sep = " ~ " ))
        
        data_imp_2 <- deparse(substitute(data_imp))
        
        fit_fn <- do.call("glm", 
                       list(formula = f, 
                            data = as.name(data_imp_2), 
                            family = "binomial"))
        
        print(summary(fit_fn))
        
        or <- as.data.frame(exp(coefficients(fit_fn)))
        or$Variable <- rownames(or)
        rownames(or) <- c()
        names(or) <- c('OddsRatio', 'Variable')

        ci <- as.data.frame(exp(confint(fit_fn)))
        ci$Variable <- rownames(ci)
        rownames(ci) <- c()

        p_val_list <- tidy(fit_fn) %>%
        select(term, p.value) %>%
        rename(Variable = term) %>%
        mutate(p.value = round(p.value, 4))
        p_val_list$p.value <- as.character(p_val_list$p.value)
        p_val_list$p.value[p_val_list$p.value == "0"] <- "< 0.0001"

        all <- full_join(or, ci, by = 'Variable')
        all <- full_join(all, p_val_list, by = "Variable")
        names(all) <- c('OddsRatio', 'Variable', 'Lower', 'Upper', "p_value")
        all <- na.omit(all)

        all <- all %>%
        filter(Variable != '(Intercept)') 


        text <- cbind(c("Variable", as.character(all$Variable)), 
              c("Odds Ratio", as.character(round(all$OddsRatio, 2))),
              c("Lower CI", as.character(round(all$Lower, 2))),
              c("Upper CI", as.character(round(all$Upper, 2))),
              c("p Value", all$p_value))


        forestplot(text, 
           mean = c(NA, all$OddsRatio), 
           lower = c(NA, all$Lower), 
           upper = c(NA, all$Upper), 
           new_page =   TRUE, zero = 1, 
           clip = c(0.1, 100),
           hrzl_lines = list("2" = gpar(col="#444444")),
           vertices = TRUE,
           graph.pos = 2,
           xlab = "Odds Ratio (log)",
           align = "c",
           txt_gp = fpTxtGp(cex = 0.7),
           xticks = getTicks(low = all$Lower,
                             high = all$Upper,
                             clip=c(-Inf, Inf),
                             exp=TRUE),
           boxsize = 0.1)
    
}
```

```{r chunk2, cache=TRUE, message=FALSE, warning=FALSE, results='hide'}
col.width <- c(37, 10, 1, 1, 3, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 8, 2, 2, 2, 4, 4, 1, 4, 1, 1,
               1, 3, 2, 2, 8, 2, 5, 5, 5, 4, 5, 5, 5,4, 2, 1, 2, 1, 3, 1, 1, 1, 1, 1, 1, 3,
               3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 8,
               8, 8, 2, 1, 1, 1, 1, 8, 1, 1, 8, 1, 1, 2, 2, 5, 2, 5, 3, 1, 3, 1, 8, 8, 2, 8,
               2, 8, 2, 2, 1, 8, 1, 1, 1, 1, 1, 8, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 3, 1, 1,
               1, 1, 1, 1, 1, 1, 1)

col.names.abr <- c("PUF_CASE_ID", "PUF_FACILITY_ID", "FACILITY_TYPE_CD", "FACILITY_LOCATION_CD",
                   "AGE", "SEX", "RACE", "SPANISH_HISPANIC_ORIGIN", "INSURANCE_STATUS",
                   "MED_INC_QUAR_00", "NO_HSD_QUAR_00", "UR_CD_03", "MED_INC_QUAR_12", "NO_HSD_QUAR_12",
                   "UR_CD_13", "CROWFLY", "CDCC_TOTAL_BEST", "SEQUENCE_NUMBER", "CLASS_OF_CASE",
                   "YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "LATERALITY", "HISTOLOGY", "BEHAVIOR", "GRADE",
                   "DIAGNOSTIC_CONFIRMATION", "TUMOR_SIZE", "REGIONAL_NODES_POSITIVE",
                   "REGIONAL_NODES_EXAMINED", "DX_STAGING_PROC_DAYS", "RX_SUMM_DXSTG_PROC", "TNM_CLIN_T",
                   "TNM_CLIN_N", "TNM_CLIN_M", "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                   "TNM_PATH_STAGE_GROUP", "TNM_EDITION_NUMBER", "ANALYTIC_STAGE_GROUP", "CS_METS_AT_DX",
                   "CS_METS_EVAL", "CS_EXTENSION", "CS_TUMOR_SIZEEXT_EVAL", "CS_METS_DX_BONE", "CS_METS_DX_BRAIN",
                   "CS_METS_DX_LIVER", "CS_METS_DX_LUNG", "LYMPH_VASCULAR_INVASION", "CS_SITESPECIFIC_FACTOR_1",
                   "CS_SITESPECIFIC_FACTOR_2", "CS_SITESPECIFIC_FACTOR_3", "CS_SITESPECIFIC_FACTOR_4",
                   "CS_SITESPECIFIC_FACTOR_5", "CS_SITESPECIFIC_FACTOR_6", "CS_SITESPECIFIC_FACTOR_7",
                   "CS_SITESPECIFIC_FACTOR_8", "CS_SITESPECIFIC_FACTOR_9", "CS_SITESPECIFIC_FACTOR_10",
                   "CS_SITESPECIFIC_FACTOR_11", "CS_SITESPECIFIC_FACTOR_12", "CS_SITESPECIFIC_FACTOR_13",
                   "CS_SITESPECIFIC_FACTOR_14", "CS_SITESPECIFIC_FACTOR_15", "CS_SITESPECIFIC_FACTOR_16",
                   "CS_SITESPECIFIC_FACTOR_17", "CS_SITESPECIFIC_FACTOR_18", "CS_SITESPECIFIC_FACTOR_19",
                   "CS_SITESPECIFIC_FACTOR_20", "CS_SITESPECIFIC_FACTOR_21", "CS_SITESPECIFIC_FACTOR_22",
                   "CS_SITESPECIFIC_FACTOR_23", "CS_SITESPECIFIC_FACTOR_24", "CS_SITESPECIFIC_FACTOR_25",
                   "CS_VERSION_LATEST", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS", "DX_DEFSURG_STARTED_DAYS",
                   "RX_SUMM_SURG_PRIM_SITE", "RX_HOSP_SURG_APPR_2010", "RX_SUMM_SURGICAL_MARGINS",
                   "RX_SUMM_SCOPE_REG_LN_SUR", "RX_SUMM_SURG_OTH_REGDIS", "SURG_DISCHARGE_DAYS", "READM_HOSP_30_DAYS",
                   "REASON_FOR_NO_SURGERY", "DX_RAD_STARTED_DAYS", "RX_SUMM_RADIATION", "RAD_LOCATION_OF_RX",
                   "RAD_TREAT_VOL", "RAD_REGIONAL_RX_MODALITY", "RAD_REGIONAL_DOSE_CGY", "RAD_BOOST_RX_MODALITY",
                   "RAD_BOOST_DOSE_CGY", "RAD_NUM_TREAT_VOL", "RX_SUMM_SURGRAD_SEQ", "RAD_ELAPSED_RX_DAYS",
                   "REASON_FOR_NO_RADIATION", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS", "RX_SUMM_CHEMO",
                   "DX_HORMONE_STARTED_DAYS", "RX_SUMM_HORMONE", "DX_IMMUNO_STARTED_DAYS", "RX_SUMM_IMMUNOTHERAPY",
                   "RX_SUMM_TRNSPLNT_ENDO", "RX_SUMM_SYSTEMIC_SUR_SEQ", "DX_OTHER_STARTED_DAYS", "RX_SUMM_OTHER",
                   "PALLIATIVE_CARE", "RX_SUMM_TREATMENT_STATUS", "PUF_30_DAY_MORT_CD", "PUF_90_DAY_MORT_CD",
                   "DX_LASTCONTACT_DEATH_MONTHS", "PUF_VITAL_STATUS", "RX_HOSP_SURG_PRIM_SITE", "RX_HOSP_CHEMO",
                   "RX_HOSP_IMMUNOTHERAPY", "RX_HOSP_HORMONE", "RX_HOSP_OTHER", "PUF_MULT_SOURCE", "REFERENCE_DATE_FLAG",
                   "RX_SUMM_SCOPE_REG_LN_2012", "RX_HOSP_DXSTG_PROC", "PALLIATIVE_CARE_HOSP", "TUMOR_SIZE_SUMMARY",
                   "METS_AT_DX_OTHER", "METS_AT_DX_DISTANT_LN", "METS_AT_DX_BONE", "METS_AT_DX_BRAIN",
                   "METS_AT_DX_LIVER", "METS_AT_DX_LUNG", "NO_HSD_QUAR_16", "MED_INC_QUAR_16", "MEDICAID_EXPN_CODE")



#Read in data for each subsite
lip <- read_fwf('NCDBPUF_Lip.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

melanoma <- read_fwf('NCDBPUF_Melanoma.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
                       
skin <- read_fwf('NCDBPUF_OtSkin.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

hodgextr <- read_fwf('NCDBPUF_HodgExtr.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

hodgndal <- read_fwf('NCDBPUF_HodgNdal.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

NHLndal <- read_fwf('NCDBPUF_NHLNdal.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

NHLextr <- read_fwf('NCDBPUF_NHLExtr.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))


breast <-  read_fwf('NCDBPUF_Breast.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

vulva <-  read_fwf('NCDBPUF_Vulva.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

vagina <-  read_fwf('NCDBPUF_Vagina.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

penis <-  read_fwf('NCDBPUF_Penis.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

otleuk <- read_fwf('NCDBPUF_OtLeuk.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
  
otheracuteleuk  <- read_fwf('NCDBPUF_OtAcLeuk.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
  
ALL  <- read_fwf('NCDBPUF_ALymLeuk.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))


#Combine data for all subsites
dat <- bind_rows(lip, melanoma, skin, hodgextr, hodgndal, NHLndal, breast, 
                 vulva, vagina, penis, NHLextr, otleuk, otheracuteleuk, ALL)

rm(lip, melanoma, skin, hodgextr, hodgndal, NHLndal, breast, vulva, vagina, 
   penis, NHLextr, otleuk, otheracuteleuk, ALL)

prim_site_text <- data_frame(PRIMARY_SITE = c(
#NHL sites
"C000", 
"C001", 
"C002", 
"C003", 
"C004", 
"C005", 
"C006", 
"C008",
"C009", 
"C019", 
"C020", 
"C021",
"C022", 
"C023", 
"C024", 
"C028", 
"C029",
"C030",
"C031",
"C039", 
"C040", 
"C041", 
"C048",
"C049", 
"C050", 
"C051", 
"C052", 
"C058", 
"C059",
"C060", 
"C061", 
"C062", 
"C068", 
"C069", 
"C079",  
"C098",
"C099",
"C111",
"C142",
"C300",
"C379",
"C420",
"C422",
"C424",

#skin/melanoma
                                 "C440", "C441", "C442", "C443", "C444", "C445",
                                 "C446", "C447", "C448", "C449",
                                 
                                 #breast - nipple
                                 "C500",
                                 
                                 #vagina/vulva
                                 "C510", "C511", "C512", "C518", "C519", "C529",
                                 
                                 #penis
                                 "C600", "C601", "C602", "C608", "C609", "C639",

"C770",
"C771",
"C772",
"C773",
"C774",
"C775",
"C778",
"C779"),

SITE_TEXT = c(
"C00.0 External Lip: Upper NOS",
"C00.1 External Lip: Lower NOS",
"C00.2 External Lip: NOS",
"C00.3 Lip: Upper Mucosa",
"C00.4 Lip: Lower Mucosa",
"C00.5 Lip: Mucosa NOS",
"C00.6 Lip: Commissure",
"C00.8 Lip: Overlapping",
"C00.9 Lip NOS",
"C01.9 Tongue: Base NOS",
"C02.0 Tongue: Dorsal NOS",
"C02.1 Tongue: Border, Tip",
"C02.2 Tongue: Ventral NOS",
"C02.3 Tongue: Anterior NOS",
"C02.4 Lingual Tonsil",
"C02.8 Tongue: Overlapping",
"C02.9 Tongue: NOS",
"C03.0 Gum: Upper",
"C03.1 Gum: Lower",
"C03.9 Gum NOS",
"C04.0 Mouth: Anterior Floor",
"C04.1 Mouth: Lateral Floor",
"C04.8 Mouth: Overlapping Floor",
"C04.9 Floor of Mouth NOS",
"C05.0 Hard Palate",
"C05.1 Soft Palate NOS",
"C05.2 Uvula",
"C05.8 Palate: Overlapping",
"C05.9 Palate NOS",
"C06.0 Cheek Mucosa",
"C06.1 Mouth: Vestibule",
"C06.2 Retromolar Area",
"C06.8 Mouth: Other Overlapping",
"C06.9 Mouth NOS",
"C07.9 Parotid Gland",
  "C09.8 Tonsil: Overlapping",
  "C09.9 Tonsil NOS",
  "C11.1 Nasopharynx: Poster Wall", 
  "C14.2 Waldeyer Ring",
  "C30.0 Nasal Cavity",
  "C37.9 Thymus",
"C42.0 Blood",
  "C42.2 Spleen",
"C42.4 Hematopoietic NOS",

 #skin
"C44.0 Skin of lip, NOS",
"C44.1 Eyelid",
"C44.2 External ear",
"C44.3 Skin of ear and unspecified parts of face",
"C44.4 Skin of scalp and neck",
"C44.5 Skin of trunk",
"C44.6 Skin of upper limb and shoulder",
"C44.7 Skin of lower limb and hip",
"C44.8 Overlapping lesion of skin",
"C44.9 Skin, NOS", 

#breast
"C50.0 Nipple",

#vulva/vagina
"C51.0 Labium majus",
"C51.1 Labium minus",
"C51.2 Clitoris",
"C51.8 Overlapping lesion of vulva",
"C51.9 Vulva, NOS",
"C52.9 Vagina, NOS",

#penis
"C60.0 Prepuce",
"C60.1 Glans penis",
"C60.2 Body of penis",
"C60.8 Overlapping lesion of penis",
"C60.9 Penis",
"C63.2 Scrotum, NOS",

  "C77.0 Lymph Nodes: HeadFaceNeck",
  "C77.1 Intrathoracic Lymph Nodes",
  "C77.2 Intra-abdominal LymphNodes",
  "C77.3 Lymph Nodes of axilla or arm ",
  "C77.4 Lymph Nodes: Leg",
  "C77.5 Pelvic Lymph Nodes",
  "C77.8 Lymph Nodes: multiple region",
  "C77.9 Lymph Node NOS"))


dat <- merge(dat, prim_site_text, by = "PRIMARY_SITE", all.x = TRUE) 

rm(prim_site_text)

# convert numeric variables from character class to numeric class
num_vars <- c("AGE", "CROWFLY", "TUMOR_SIZE", "DX_STAGING_PROC_DAYS", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
              "DX_DEFSURG_STARTED_DAYS", "SURG_DISCHARGE_DAYS", "DX_RAD_STARTED_DAYS",  "RAD_REGIONAL_DOSE_CGY",
              "RAD_BOOST_DOSE_CGY", "RAD_ELAPSED_RX_DAYS", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS", 
              "DX_HORMONE_STARTED_DAYS", "DX_OTHER_STARTED_DAYS", "DX_LASTCONTACT_DEATH_MONTHS",
              "RAD_NUM_TREAT_VOL")

dat[num_vars] <- lapply(dat[num_vars], as.numeric)


# convert factor variables from character class to factor class
vars <- names(dat)
fact_vars <- vars[!(vars %in% num_vars)] # basically all of the non-numerics

dat[fact_vars] <- lapply(dat[fact_vars], as.character)
dat[fact_vars] <- lapply(dat[fact_vars], as.factor)

dat <- dat %>%
        mutate(FACILITY_TYPE_F = fct_recode(FACILITY_TYPE_CD,
                                            "Community Cancer Program" = "1",
                                            "Comprehensive Comm Ca Program" = "2",
                                            "Academic/Research Program" = "3",
                                            "Integrated Network Ca Program" = "4",
                                            "Other" = "9")) %>%
        mutate(FACILITY_LOCATION_F = fct_recode(FACILITY_LOCATION_CD,
                                            "New England" = "1",
                                            "Middle Atlantic" = "2",
                                            "South Atlantic" = "3",
                                            "East North Central" = "4",
                                            "East South Central" = "5",
                                            "West North Central" = "6",
                                            "West South Central" = "7",
                                            "Mountain" = "8",
                                            "Pacific" = "9",
                                            "out of US" = "0")) %>%
        mutate(FACILITY_GEOGRAPHY = fct_collapse(FACILITY_LOCATION_CD,
                                                 "Northeast" = c("1", "2"),
                                                 "South" = c("3", "7"),
                                                 "Midwest" = c("4", "5", "6"),
                                                 "West" = c("8", "9"))) %>%
        mutate(AGE_F = cut(AGE, c(0, 54, 64, 74, 100))) %>%
        mutate(AGE_40 = cut(AGE, c(0, 40, 100))) %>%
        mutate(SEX_F = fct_recode(SEX,
                                "Male" = "1",
                                "Female" = "2")) %>%
        mutate(RACE_F = fct_collapse(RACE,
                                "White" = c("01"),
                                "Black" = c("02"),
                                "Asian" = c("04", "05", "06", "07", "08", "10", "11", "12", "13", "14", "15",
                                            "16", "17", "20", "21", "22", "25", "26", "27", "28", "30", "31",
                                            "32", "96", "97"),
                                "Other/Unk" = c("03", "98", "99"))) %>%
        mutate(HISPANIC = fct_collapse(SPANISH_HISPANIC_ORIGIN,
                                       "Yes" = c("1", "2", "3", "4", "5", "6", "7", "8"),
                                       "No" = c("0"),
                                       "Unknown" = c("9"))) %>%
        mutate(INSURANCE_F = fct_recode(INSURANCE_STATUS,
                                         "None" = "0",
                                         "Private" = "1",
                                         "Medicaid" = "2",
                                         "Medicare" = "3",
                                         "Other Government" = "4",
                                         "Unknown" = "9")) %>%
        mutate(INSURANCE_F = fct_relevel(INSURANCE_F,
                                         "Private")) %>%
        mutate(INCOME_F = fct_recode(MED_INC_QUAR_12,
                                      "Less than $38,000" = "1",
                                      "$38,000 - $47,999" = "2",
                                      "$48,000 - $62,999" = "3",
                                      "$63,000 +" = "4")) %>%
        mutate(EDUCATION_F = fct_recode(NO_HSD_QUAR_12,
                                        "21% or more" = "1",
                                        "13 - 20.9%" = "2",
                                        "7 - 12.9%" = "3",
                                        "Less than 7%" = "4")) %>%
        mutate(U_R_F = fct_collapse(UR_CD_13,
                                    "Metro" = c("1", "2", "3"),
                                    "Urban" = c("4", "5", "6", "7"),
                                    "Rural" = c("8", "9"))) %>%
        mutate(CLASS_OF_CASE_F = fct_collapse(CLASS_OF_CASE,
                                              All_Part_Prim = c("10", "11", "12", "13",
                                                                "14", "20", "21", "22"),
                                              Other_Facility = c("00"))) %>%
        mutate(GRADE_F = fct_recode(GRADE,
                                  "Gr I: Well Diff" = "1",
                                  "Gr II: Mod Diff" = "2",
                                  "Gr III: Poor Diff" = "3",
                                  "Gr IV: Undiff/Anaplastic" = "4",
                                  "NA/Unkown" = "9")) %>%
        mutate(HISTOLOGY_F = fct_infreq(HISTOLOGY)) %>%
        mutate(HISTOLOGY_F = factor(HISTOLOGY_F)) %>%
        mutate(HISTOLOGY_F_LIM = fct_lump(HISTOLOGY_F, prop = 0.05)) %>%
        mutate(TNM_CLIN_T = fct_recode(TNM_CLIN_T,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_T = fct_relevel(TNM_CLIN_T,
                                        "1")) %>%
        mutate(TNM_CLIN_N = fct_recode(TNM_CLIN_N,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_M = fct_recode(TNM_CLIN_M,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_T = fct_recode(TNM_PATH_T,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_T = fct_relevel(TNM_PATH_T,
                                        "1")) %>%
        mutate(TNM_PATH_N = fct_recode(TNM_PATH_N,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_M = fct_recode(TNM_PATH_M,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_STAGE_GROUP = fct_recode(TNM_CLIN_STAGE_GROUP,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_STAGE_GROUP = fct_recode(TNM_PATH_STAGE_GROUP,
                                       "N_A" = "88")) %>%
        mutate(MARGINS = fct_recode(RX_SUMM_SURGICAL_MARGINS,
                                    "No Residual" = "0",
                                    "Residual, NOS" = "1",
                                    "Microscopic Resid" = "2",
                                    "Macroscopic Resid" = "3",
                                    "Not evaluable" = "7",
                                    "No surg" = "8",
                                    "Unknown" = "9")) %>%
        mutate(MARGINS_YN = fct_collapse(RX_SUMM_SURGICAL_MARGINS,
                                         "Yes" = c("1", "2", "3"),
                                         "No" = c("0"),
                                         "No surg/Unk/NA" = c("7", "8", "9"))) %>%
        mutate(READM_HOSP_30_DAYS_F = fct_recode(READM_HOSP_30_DAYS,
                                                 "No_Surg_or_No_Readmit" = "0",
                                                 "Unplan_Readmit_Same" = "1",
                                                 "Plan_Readmit_Same" = "2",
                                                 "PlanUnplan_Same" = "3",
                                                 "Unknown" = "4")) %>%
        mutate(RX_SUMM_RADIATION_F = fct_recode(RX_SUMM_RADIATION,
                                                "None" = "0",
                                                "Beam Radiation" = "1",
                                                "Radioactive Implants" = "2",
                                                "Radioisotopes" = "3",
                                                "Beam + Imp or Isotopes" = "4",
                                                "Radiation, NOS" = "5",
                                                "Unknown" = "9")) %>%
        mutate(PUF_30_DAY_MORT_CD_F = fct_recode(PUF_30_DAY_MORT_CD,
                                                 "Alive_30" = "0",
                                                 "Dead_30" = "1",
                                                 "Unknown" = "9")) %>%
        mutate(PUF_90_DAY_MORT_CD_F = fct_recode(PUF_90_DAY_MORT_CD,
                                                 "Alive_90" = "0",
                                                 "Dead_90" = "1",
                                                 "Unknown" = "9")) %>%
        mutate(LYMPH_VASCULAR_INVASION_F = fct_recode(LYMPH_VASCULAR_INVASION,
                                                      "Neg_LymphVasc_Inv" = "0",
                                                      "Pos_LumphVasc_Inv" = "1",
                                                      "N_A" = "8",
                                                      "Unknown" = "9")) %>%
        mutate(RX_HOSP_SURG_APPR_2010_F = fct_recode(RX_HOSP_SURG_APPR_2010,
                                                     "No_Surg" = "0",
                                                     "Robot_Assist" = "1",
                                                     "Robot_to_Open" = "2",
                                                     "Endo_Lap" = "3",
                                                     "Endo_Lap_to_Open" = "4",
                                                     "Open_Unknown" = "5",
                                                     "Unknown" = "9")) %>%
        mutate(All = "All") %>%
        mutate(All = factor(All)) %>%
        mutate(REASON_FOR_NO_SURGERY_F = fct_recode(REASON_FOR_NO_SURGERY,
                                                    "Surg performed" = "0",
                                                    "Surg not recommended" = "1",
                                                    "No surg due to pt factors" = "2",
                                                    "No surg, pt died" = "5",
                                                    "Surg rec, not done" = "6",
                                                    "Surg rec, pt refused" = "7",
                                                    "Surg rec, unk if done" = "8",
                                                    "Unknown" = "9")) %>%
        mutate(SURGERY_YN = ifelse(REASON_FOR_NO_SURGERY == "0",
                                   "Yes",
                                   ifelse(REASON_FOR_NO_SURGERY == "9",
                                          "Ukn",
                                          "No"))) %>%
        mutate(SURG_TF = case_when(SURGERY_YN == "Yes" ~ TRUE,
                             SURGERY_YN == "No" ~ FALSE,
                             TRUE ~ NA))  %>%
        mutate(REASON_FOR_NO_RADIATION_F = fct_recode(REASON_FOR_NO_RADIATION,
                                                    "Rad performed" = "0",
                                                    "Rad not recommended" = "1",
                                                    "No Rad due to pt factors" = "2",
                                                    "No Rad, pt died" = "5",
                                                    "Rad rec, not done" = "6",
                                                    "Rad rec, pt refused" = "7",
                                                    "Rad rec, unk if done" = "8",
                                                    "Unknown" = "9")) %>%
        mutate(RADIATION_YN = ifelse(REASON_FOR_NO_RADIATION == "0",
                                   "Yes",
                                   ifelse(REASON_FOR_NO_RADIATION == "9",
                                          NA,
                                          "No"))) %>%
        mutate(SURGRAD_SEQ_F = fct_recode(RX_SUMM_SURGRAD_SEQ,
                                                   "None or Surg or Rad" = "0",
                                                   "Rad before Surg" = "2",
                                                   "Surg before Rad" = "3",
                                                   "Rad before and after Surg" = "4",
                                                   "Intraop Rad" = "5",
                                                   "Intraop Rad plus other" = "6",
                                                   "Unknown" = "9")) %>%
        mutate(SURG_RAD_SEQ = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
                                     "Surg Alone",
                                     ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
                                            "Rad Alone",
                                            ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0",
                                                   "No Treatment",
                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "2",
                                                          "Rad then Surg",
                                                          ifelse(RX_SUMM_SURGRAD_SEQ == "3",
                                                                 "Surg then Rad",
                                                                 ifelse(RX_SUMM_SURGRAD_SEQ == "4",
                                                                        "Rad before and after Surg",
                                                                        "Other"))))))) %>%
        mutate(SURG_RAD_SEQ = fct_relevel(SURG_RAD_SEQ,
                                          "Surg Alone",
                                          "Surg then Rad",
                                          "Rad Alone")) %>%
        mutate(CHEMO_YN = fct_collapse(RX_SUMM_CHEMO,
                                       "No" = c("00", "82", "85", "86", "87"),
                                       "Yes" = c("01", "02", "03"),
                                       "Ukn" = c("88", "99"))) %>%
        mutate(IMMUNO_YN = fct_collapse(RX_SUMM_IMMUNOTHERAPY,
                                       "No" = c("00", "82", "85", "86", "87"),
                                       "Yes" = c("01"),
                                       "Ukn" = c("88", "99"))) %>%
        mutate(SURG_RAD_SEQ_C = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                     "Surg, No rad, No Chemo",
                                     ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                            "Rad, No Surg, No Chemo",
                                            ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                                   "No Surg, No Rad, No Chemo",
                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "No",
                                                          "Rad then Surg, No Chemo",
                                                          ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "No",
                                                                 "Surg then Rad, No Chemo",
                                                                 ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "No",
                                                                        "Rad before and after Surg, No Chemo",
                                ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                       "Surg, No rad, Yes Chemo",
                                       ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                              "Rad, No Surg, Yes Chemo",
                                              ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                                     "No Surg, No Rad, Yes Chemo",
                                                     ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "Yes",
                                                            "Rad then Surg, Yes Chemo",
                                                            ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "Yes",
                                                                   "Surg then Rad, Yes Chemo",
                                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "Yes",
                                                                          "Rad before and after Surg, Yes Chemo",
                                                                          "Other"))))))))))))) %>%
        mutate(SURG_RAD_SEQ_C = fct_infreq(SURG_RAD_SEQ_C)) %>%
        mutate(T_SIZE = as.numeric(TUMOR_SIZE)) %>%
        mutate(T_SIZE = ifelse(T_SIZE == 0,
                                "No Tumor",
                                ifelse(T_SIZE > 0 & T_SIZE < 10 | T_SIZE == 991,
                                       "< 1 cm",
                                       ifelse(T_SIZE >= 10 & T_SIZE < 20 | T_SIZE == 992,
                                              "1-2 cm",
                                              ifelse(T_SIZE >= 20 & T_SIZE < 30 | T_SIZE == 993,
                                                     "2-3 cm",
                                                     ifelse(T_SIZE >= 30 & T_SIZE < 40 | T_SIZE == 994,
                                                            "3-4 cm",
                                                            ifelse(T_SIZE >= 40 & T_SIZE < 50 | T_SIZE == 995,
                                                                   "4-5 cm",
                                                                   ifelse(T_SIZE >= 50 & T_SIZE < 60 | T_SIZE == 996,
                                                                          "5-6 cm",
                                                                          ifelse(T_SIZE >= 60 & T_SIZE <= 987 |
                                                                                         T_SIZE == 980 | T_SIZE == 989 |
                                                                                         T_SIZE == 997,
                                                                          ">6 cm",
                                                                          ifelse(T_SIZE == 988 | T_SIZE == 999,
                                                                                 "NA_unk",
                                                                                 "Microscopic focus")))))))))) %>%
        mutate(T_SIZE = factor(T_SIZE)) %>%
        mutate(T_SIZE = fct_relevel(T_SIZE,
                                     "No Tumor", "Microscopic focus", "< 1 cm", "1-2 cm", "2-3 cm", "3-4 cm",
                                       "4-5 cm", "5-6 cm", ">6 cm", "NA_unk")) %>%
        mutate(mets_at_dx = case_when(CS_METS_DX_LUNG == "1" ~ "Lung",
                                      CS_METS_DX_BONE == "1" ~ "Bone",
                                      CS_METS_DX_BRAIN == "1" ~ "Brain",
                                      CS_METS_DX_LIVER == "1" ~ "Liver",
                                      TRUE ~ "None/Other/Unk/NA")) %>%
        mutate(MEDICAID_EXPN_CODE = fct_recode(MEDICAID_EXPN_CODE,
                                               "Non-Expansion State" = "0",
                                               "Jan 2014 Expansion States" = "1",
                                               "Early Expansion States (2010-13)" = "2",
                                               "Late Expansion States (> Jan 2014)" = "3",
                                               "Suppressed for Ages 0 - 39" = "9"))  %>%
        mutate(EXPN_GROUP =  case_when(MEDICAID_EXPN_CODE  %in% c("Jan 2014 Expansion States") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Post-Expansion",
                                       
                                       MEDICAID_EXPN_CODE  %in% c("Jan 2014 Expansion States") & 
                                         YEAR_OF_DIAGNOSIS %in% 
                                          c("2004", "2005", "2006", "2007", "2008", 
                                            "2009", "2010", "2011", "2012", "2013") ~ "Pre-Expansion",
               
                                       MEDICAID_EXPN_CODE  %in% c("Early Expansion States (2010-13)") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2010", "2011", "2012", "2013", "2014", "2015") ~ "Post-Expansion",
                                       
                                        MEDICAID_EXPN_CODE  %in% c("Early Expansion States (2010-13)") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008", "2009") ~ "Pre-Expansion",

                                       MEDICAID_EXPN_CODE %in% c("Non-Expansion State") ~ "Pre-Expansion",

                                       MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") ~ "Pre-Expansion",
                    
                                       MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") & 
                                        YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Exclude",
                                       
                                       MEDICAID_EXPN_CODE == "Suppressed for Ages 0 - 39" ~ "Exclude")) %>%
  
  mutate(pre_2014 = YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008", 
                                            "2009", "2010", "2011", "2012", "2013")) %>%
  
  mutate(mets_at_dx_F = ifelse(mets_at_dx == "None/Other/Unk/NA", FALSE, TRUE)) %>% 
  
  mutate(Tx_YN = ifelse(SURG_RAD_SEQ == "No Treatment" & CHEMO_YN == "No" & 
                          IMMUNO_YN == "No", FALSE, 
                        ifelse(CHEMO_YN == "Ukn", NA, 
                               TRUE)))

fact_vars_2 <- c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "AGE_F", "SEX_F", "RACE_F",
                 "HISPANIC", "INSURANCE_F", "INCOME_F", "EDUCATION_F", "U_R_F",
                 "CDCC_TOTAL_BEST", "CLASS_OF_CASE_F", "YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "HISTOLOGY",
                 "BEHAVIOR", "GRADE_F", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M", "TNM_PATH_STAGE_GROUP",
                 "MARGINS", "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "mets_at_dx")


dat <- dat %>%
        mutate_at(fact_vars_2, funs(factor(.)))

```


# Extract data of interest 

```{r}
# MPD
site_code <- c(
 #breast
  "C500", "C501", "C502","C503","C504","C505",
                                 "C506","C508","C509")

histo_code <- c("8540")
behavior_code <- c("3")

data <- dat %>%
        filter(BEHAVIOR %in% behavior_code) %>%
        filter(PRIMARY_SITE %in% site_code) %>%
        filter(HISTOLOGY %in% histo_code) %>%
#        filter(AGE >= 18) %>%
        filter(is.na(PUF_VITAL_STATUS) == FALSE) %>%
        filter(is.na(DX_LASTCONTACT_DEATH_MONTHS) == FALSE) %>%
        filter(SEQUENCE_NUMBER == "00") 

no_Excludes <- as.data.frame(data %>% 
                               filter(EXPN_GROUP != "Exclude") 
                             %>% droplevels())


#file_path <- c("/Users/beastatlife/Google Drive/Penn/Research/Barbieri/NCDB")
#save(data,
#      file = paste0(file_path, "/breast_data.Rda"))
```

```{r loadData2}
#load("EMPD_data.Rda")
```




Data including skin tumors was obtained from the NCBD on October 7, 2019. Cases that were included in this analysis were those with:

1. Site codes: `r site_code`
2. Histology codes: `r histo_code`
3. Behavior codes: `r behavior_code`


Patients were excluded if they didn't have values for either follow up or vital status.

Patients were excluded if they had surgery to a distant site using `RX_SUMM_SURG_OTH_REGDIS`. This was done to avoid confounding of different surgical procedures. We are only interested in surgery at the primary site. These distant site surgeries were being counted in the surgery/radiation sequence and thus to simplify analysis they were removed. 

```{r}

data %>%
        CreateTableOne(data = .,
                     vars = c("RX_SUMM_SURG_OTH_REGDIS"),
                     includeNA = TRUE) %>%
        print(.,
              showAllLevels = TRUE)

data <- data %>%
        filter(RX_SUMM_SURG_OTH_REGDIS == "0") 
```


Race was grouped as white, black, asian, other/unknown
Stage was grouped into 0, I, II, III, IV, NA_Unknown, stage 0 was removed
Whether surgery was performed was based on the `REASON_FOR_NO_SURGERY` variable. The `SURGERY_YN` variable was classified as 'Yes', 'No', or 'Unknown'.


Whether radiation was performed was based on the `REASON_FOR_NO_RADIATION` variable. The `RADIATION_YN` variable was classified as 'Yes', 'No', or 'Unknown'.



 ##Table of variables for all cases:

```{r}

p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT",  "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "SURGERY_YN", "RADIATION_YN", "CHEMO_YN", 
                 "IMMUNO_YN", "Tx_YN", "mets_at_dx",
                 "MEDICAID_EXPN_CODE", "EXPN_GROUP"))

```

```{r}
p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN", "CHEMO_YN",
                 "IMMUNO_YN", "Tx_YN", "mets_at_dx",
                 "MEDICAID_EXPN_CODE"), 
        strata = "SURGERY_YN")


p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN", 
                 "CHEMO_YN", "IMMUNO_YN", "Tx_YN", "mets_at_dx",
                 "MEDICAID_EXPN_CODE"), 
        strata = "RADIATION_YN")

p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN", 
                 "CHEMO_YN", "IMMUNO_YN", "Tx_YN","mets_at_dx",
                 "MEDICAID_EXPN_CODE"), 
        strata = "CHEMO_YN")


p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN", 
                 "CHEMO_YN", "mets_at_dx", "IMMUNO_YN", "Tx_YN",
                 "MEDICAID_EXPN_CODE"), 
        strata = "Tx_YN")
```







#Kaplan Meier Analysis


##All

```{r}
uni_var(test_var = "All", data_imp = data)
```

##Facility Type
```{r}
uni_var(test_var = "FACILITY_TYPE_F", data_imp = data)
```

##Facility Location

```{r}
uni_var(test_var = "FACILITY_LOCATION_F", data_imp = data)
```

##Facility Geography

```{r}
uni_var(test_var = "FACILITY_GEOGRAPHY", data_imp = data)
```

##Age Group

```{r}
uni_var(test_var = "AGE_F", data_imp = data)
```

##Age Group
```{r}
uni_var(test_var = "AGE_40", data_imp = data)
```

##Gender

```{r}
uni_var(test_var = "SEX_F", data_imp = data)
```

##RACE_F

```{r}
uni_var(test_var = "RACE_F", data_imp = data)
```

##Hispanic

```{r}
uni_var(test_var = "HISPANIC", data_imp = data)
```

##Insurance Status

```{r}
uni_var(test_var = "INSURANCE_F", data_imp = data)
```

##Overall Survival pre/post-ACA expansion

```{r}
uni_var(test_var = "EXPN_GROUP", data_imp = no_Excludes)
```


<!-- ##Income -->

<!-- ```{r} -->
<!-- class(data$INCOME_F) -->
<!-- uni_var(test_var = "INCOME_F", data_imp = data) -->
<!-- ``` -->

##Education

```{r}
uni_var(test_var = "EDUCATION_F", data_imp = data)
```

##Urban/Rural

```{r}
uni_var(test_var = "U_R_F", data_imp = data)
```

##Class (treatment at performing facility)

```{r}
uni_var(test_var = "CLASS_OF_CASE_F", data_imp = data)
```

##Year

```{r}
uni_var(test_var = "YEAR_OF_DIAGNOSIS", data_imp = data)
```

<!-- ##Primary Site -->

<!-- ```{r} -->
<!-- uni_var(test_var = "SITE_TEXT", data_imp = data) -->
<!-- ``` -->


##Histology

```{r}
#uni_var(test_var = "HISTOLOGY_F_LIM", data_imp = data)
```

<!-- ##Behavior -->

<!-- ```{r} -->
<!-- uni_var(test_var = "BEHAVIOR", data_imp = data) -->
<!-- ``` -->

##Grade

```{r}
uni_var(test_var = "GRADE_F", data_imp = data)
```

##Clinical T Stage

```{r}
#uni_var(test_var = "TNM_CLIN_T", data_imp = data)
```

##Clinical N Stage

```{r}
#uni_var(test_var = "TNM_CLIN_N", data_imp = data)
```

##Clinical M Stage

```{r}
#uni_var(test_var = "TNM_CLIN_M", data_imp = data)
```

##Clinical Stage Group

```{r}
uni_var(test_var = "TNM_CLIN_STAGE_GROUP", data_imp = data)
```

##Pathologic T Stage

```{r}
uni_var(test_var = "TNM_PATH_T", data_imp = data)
```

##Pathologic N Stage

```{r}
uni_var(test_var = "TNM_PATH_N", data_imp = data)
```

##Pathologic M Stage

```{r}
uni_var(test_var = "TNM_PATH_M", data_imp = data)
```

##Pathologic Stage Group

```{r}
uni_var(test_var = "TNM_PATH_STAGE_GROUP", data_imp = data)
```

##Margins
```{r}
uni_var(test_var = "MARGINS", data_imp = data)
```

##Margins Yes/No
```{r}
#uni_var(test_var = "MARGINS_YN", data_imp = data)
```

##30 Day Readmission

```{r}
uni_var(test_var = "READM_HOSP_30_DAYS_F", data_imp = data)
```

##Radiation Type

```{r}
uni_var(test_var = "RX_SUMM_RADIATION_F", data_imp = data)
```


##Lymphovascular Invasion

```{r}
uni_var(test_var = "LYMPH_VASCULAR_INVASION_F", data_imp = data)
```

##Endoscopic/Robotic

```{r}
uni_var(test_var = "RX_HOSP_SURG_APPR_2010_F", data_imp = data)
```

##Surgery Radiation Sequence 

```{r}
uni_var(test_var = "SURG_RAD_SEQ", data_imp = data)
```

##Surgery Yes/No

```{r}
uni_var(test_var = "SURGERY_YN", data_imp = data)
```

##Radiation Yes/No

```{r}
uni_var(test_var = "RADIATION_YN", data_imp = data)
```

##Chemo Yes/No

```{r}
uni_var(test_var = "CHEMO_YN", data_imp = data)
```


##Treatment Yes/No
```{r}
uni_var(test_var = "Tx_YN", data_imp = data)
```

##Metastases at Dx
```{r}
uni_var(test_var = "mets_at_dx_F", data_imp = data)
```

##Tumor Size

```{r}
uni_var(test_var = "T_SIZE", data_imp = data)
```

#Tumor specific Variables


###Node Size


#Cox Proportional Hazard Ratio

##Model #1

###Full analysis

```{r}
model_one <- coxph(Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 0)
                     ~ FACILITY_TYPE_F + FACILITY_LOCATION_F + CROWFLY + 
                 DX_STAGING_PROC_DAYS + 
                 CHEMO_YN + RADIATION_YN + SURGERY_YN + IMMUNO_YN +
                 AGE_F + SEX_F + RACE_F + HISPANIC + INSURANCE_F + INCOME_F + 
                 EDUCATION_F + YEAR_OF_DIAGNOSIS,
                     data = data)
model_one %>% summary()


```

###Summary of Model

```{r}
model_one %>%
        tidy(., exponentiate = TRUE) %>%
        select(term, estimate, conf.low, conf.high, p.value) %>%
        rename(Variable = term,
               Hazard_Ratio = estimate) %>%
        tbl_df %>%
        print(n = nrow(.))

```

## Linear Regression 
```{r}

#only include rows with known treatment information, n = 82
data2 <- data %>% filter(SURGERY_YN != "Ukn" & RADIATION_YN != "Ukn"
                         & CHEMO_YN != "Ukn" & IMMUNO_YN != "Ukn")

# include only variables with data available for at least 75% cases 
# from the following set of variables of interest:

## FACILITY_TYPE_F + FACILITY_GEOGRAPHY + CROWFLY + 
##                 DX_STAGING_PROC_DAYS + 
##                 CHEMO_YN + RADIATION_YN + SURGERY_YN + IMMUNO_YN +
##                 AGE + SEX_F + RACE_F + HISPANIC + INSURANCE_F + INCOME_F + 
##                 EDUCATION_F + YEAR_OF_DIAGNOSIS + SITE_TEXT + GRADE_F

length(which(is.na(data2$SITE_TEXT))) / nrow(data2)

# excluded the following in this analysis due to missing data: 
#  DX_STAGING_PROC_DAYS, GRADE_F (mostly unknowns), SITE_TEXT

fit_surv <- lm(DX_LASTCONTACT_DEATH_MONTHS ~
                 FACILITY_TYPE_F + 
                 CHEMO_YN + SURGERY_YN +
                 AGE_F + INSURANCE_F + INCOME_F + 
                 EDUCATION_F + YEAR_OF_DIAGNOSIS,
   data = data2)

summary(fit_surv) # R^2 = 0.4207, p < 2.2e-16

# the following variables were excluded to 
# improve the R-squared of the regression (initially R^2 = 0.4106):
#  HISPANIC, RACE_F, FACILITY_LOCATION_F, CROWFLY, RADIATION_YN, IMMUNO_YN, SEX_F
```

# Prediction Logistic Regression Models

## Surgery
```{r}

no_Ukns <- data2 %>%
  droplevels() %>% 
  mutate(SURGERY_YN = as.logical(SURGERY_YN))

# excluded the following in this analysis due to missing data: 
#  DX_STAGING_PROC_DAYS, GRADE_F (mostly unknowns) + SITE_TEXT

fit_surg <- glm(SURG_TF ~ 
                 FACILITY_TYPE_F + FACILITY_LOCATION_F + 
                 CHEMO_YN + RADIATION_YN + IMMUNO_YN +
                 AGE_F + SEX_F + RACE_F + HISPANIC + INSURANCE_F + INCOME_F + 
                 EDUCATION_F + YEAR_OF_DIAGNOSIS,
   data = no_Ukns)

summary(fit_surg)

# the following variables were excluded to 
# improve the R-squared of the regression (initially residual = 72.225):
# none

exp(cbind("Odds ratio" = coef(fit_surg), confint.default(fit_surg, level = 0.95)))
```

## Metastasis at Time of Diagnosis
```{r}

fit_mets <- glm(mets_at_dx_F ~ 
                 FACILITY_TYPE_F + FACILITY_LOCATION_F + 
                 AGE_F + SEX_F + RACE_F + INSURANCE_F + INCOME_F + 
                 EDUCATION_F + YEAR_OF_DIAGNOSIS,
   data = data)


# the following variables were excluded to 
# improve the R-squared of the regression (initial residual = 12.525):
# HISPANIC + 

summary(fit_mets)

exp(cbind("Odds ratio" = coef(fit_mets), confint.default(fit_mets, level = 0.95)))
```